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Melihemin/ason-brats.ipynb

Created June 5, 2021 10:05
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ason-brats.ipynb
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/Melihemin/4a3fed980577642c1f65d0e8bf06520b/ason-brats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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"metadata": {
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},
"id": "0662M6K2uenm",
"outputId": "44bc1f68-f434-4b02-feaa-5121f0b19ce0"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tZ4c4iN2uoRJ",
"outputId": "60c94661-1a4d-4b02-dc41-fc8d507c10d6"
},
"source": [
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],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"drive sample_data\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zgyMDuJeuqSn"
},
"source": [
"import numpy\n",
"import random as r\n",
"import glob\n",
"import skimage.io as io\n",
"import matplotlib.pyplot as plt\n",
"import keras\n",
"import os"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q4-gwjyauqQh",
"outputId": "6ead08d2-656a-4984-bca2-c2b21663e004"
},
"source": [
"pip install simpleitk"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting simpleitk\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/9c/6b/85df5eb3a8059b23a53a9f224476e75473f9bcc0a8583ed1a9c34619f372/SimpleITK-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (47.4MB)\n",
"\u001b[K |████████████████████████████████| 47.4MB 64kB/s \n",
"\u001b[?25hInstalling collected packages: simpleitk\n",
"Successfully installed simpleitk-2.0.2\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DMwmiUWQuqN8",
"outputId": "a7660bf8-0955-4708-af74-a9e263ac4788"
},
"source": [
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],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Archive: /content/drive/MyDrive/trainbrats20.zip\n",
" creating: MICCAI_BraTS2020_TrainingData/\n",
" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_001/\n",
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" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_002/\n",
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" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_003/\n",
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" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_009/\n",
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" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_014/\n",
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" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_015/\n",
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" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_017/\n",
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" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_018/\n",
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" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_018/BraTS20_Training_018_t2.nii \n",
" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_019/\n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_019/BraTS20_Training_019_flair.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_019/BraTS20_Training_019_seg.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_019/BraTS20_Training_019_t1.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_019/BraTS20_Training_019_t1ce.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_019/BraTS20_Training_019_t2.nii \n",
" creating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_020/\n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_020/BraTS20_Training_020_flair.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_020/BraTS20_Training_020_seg.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_020/BraTS20_Training_020_t1.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_020/BraTS20_Training_020_t1ce.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS20_Training_020/BraTS20_Training_020_t2.nii \n",
" inflating: MICCAI_BraTS2020_TrainingData/BraTS36.zip \n",
" inflating: MICCAI_BraTS2020_TrainingData/name_mapping.csv \n",
" inflating: MICCAI_BraTS2020_TrainingData/survival_info.csv \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "OI_kFLS9uqLX"
},
"source": [
"import skimage.io as io\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import random as r\n",
"import glob\n",
"from tqdm import tqdm_notebook as tqdm\n",
"\n",
"def seg_array(path,end,label):\n",
" files = glob.glob(path+end,recursive=True)\n",
" img_liste = []\n",
" r.seed(9)\n",
" r.shuffle(files)\n",
" for file in tqdm(files):\n",
" img = io.imread(file,plugin='simpleitk')\n",
" \n",
" if label == 1:\n",
" img[img != 0 ] = 1 # tam tümör\n",
" if label == 2:\n",
" img[img != 1 ] = 0 # nekroz\n",
" if label == 3:\n",
" img[img == 2 ] = 0 # ödemsiz tümör\n",
" img[img != 0 ] = 1\n",
" if label == 4:\n",
" img[img != 4 ] = 0 # genişleyen tümör\n",
" img[img == 4 ] = 1\n",
" \n",
" img.astype('float32')\n",
" \n",
" for slice in range(60,130):\n",
" img_s = img[slice,:,:]\n",
" img_s = np.expand_dims(img_s,axis=0)\n",
" img_liste.append(img_s)\n",
" \n",
" return np.array(img_liste,np.float32) #!!!!!!!!\n",
"\n",
"\n",
"def train_array(path,end):\n",
" files = glob.glob(path+end,recursive=True)\n",
" img_liste = []\n",
" r.seed(9)\n",
" r.shuffle(files)\n",
" for file in tqdm(files):\n",
" img = io.imread(file,plugin='simpleitk')\n",
" img = (img-img.mean())/ img.std()\n",
" img.astype('float32')\n",
" \n",
" for slice in range(60,130):\n",
" img_s = img[slice,:,:]\n",
" img_s = np.expand_dims(img_s,axis=0)\n",
" img_liste.append(img_s)\n",
" \n",
" return np.array(img_liste,np.float32) #!!!!!!!!"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269,
"referenced_widgets": [
"099beaf5837d4a1d9596acd25930c050",
"12132a3de7a04507a5fa2929767e6e4b",
"a5ceec65ab82432996a1ed456f02fe34",
"4cbcd43607114d3ea30a197e7c4dfa4e",
"81cbdea6cbd44e70a9e2713ff23a7351",
"d02bc9da41144b6bb370b5719803ccdc",
"16888b7fe2b24dfc8f82e4693c9b761f",
"fe4c5d8892b64a53892def437510d46d",
"d26fe13e1d914a6eb77c500e65da5166",
"a423a6b82c684a9bbaf428b30276c059",
"6f7b35a4c5f84caf930c487970103ec1",
"506c171d65dd40b483b5741fda7967b2",
"1d8107b3ac61408ebb44fbb3fe79f343",
"38765813392e4cbd8663ae1cee7c0681",
"927aacf4573f4d8ba9640bcdf3eac0ac",
"4b306bd4a05e42479c83f640c1916e13",
"1fb5b90774644f558b65927ff5547918",
"93de45ba09b9464187490605b4d445f5",
"4cfd499fed884611ba02dac0e8becec9",
"64aea7d7bb2745edaf6399ace08a4d6f",
"f44fac2d366748dba9c972d54d8d69e7",
"43252e98c73b499a8091dbce63756c08",
"c992ca0a90784e9a863e53fe0660fc2b",
"313d26950072492faeaabc517dbd1f2a"
]
},
"id": "D3Nmq9axuqG-",
"outputId": "746fdaea-a956-46a4-e28e-2a1994b1b0cc"
},
"source": [
"flair = train_array('/content/MICCAI_BraTS2020_TrainingData/**/', '**/*flair.nii')\n",
"t2 = train_array('/content/MICCAI_BraTS2020_TrainingData/**/', '**/*t2.nii')\n",
"seg = seg_array('/content/MICCAI_BraTS2020_TrainingData/**/', '**/*seg.nii', 1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "099beaf5837d4a1d9596acd25930c050",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d26fe13e1d914a6eb77c500e65da5166",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
" del sys.path[0]\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1fb5b90774644f558b65927ff5547918",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YE3AiVBTvTsK",
"outputId": "3d18565b-2dd5-4691-e313-116c3e5086df"
},
"source": [
"print('flair shape -> ',flair.shape)\n",
"print('t2 shape -> ',t2.shape)\n",
"print('seg shape -> ',seg.shape)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"flair shape -> (2800, 1, 240, 240)\n",
"t2 shape -> (2800, 1, 240, 240)\n",
"seg shape -> (2800, 1, 240, 240)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "O69A_Sd7vTqZ",
"outputId": "630da08f-3a2b-454a-ba84-8a6beac9b272"
},
"source": [
"print('flair type -> ',flair.dtype)\n",
"print('t2 type -> ',t2.dtype)\n",
"print('seg type -> ',seg.dtype)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"flair type -> float32\n",
"t2 type -> float32\n",
"seg type -> float32\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "F6cEhjl0vToF",
"outputId": "8dcb0eaa-c66f-4a20-bd20-8f1aa986043b"
},
"source": [
"x_train = np.concatenate((flair,t2),axis=1)\n",
"x_train.dtype, x_train.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(dtype('float32'), (2800, 2, 240, 240))"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OWGrkLSjvTlw",
"outputId": "05b141bb-cf6d-4bf9-d691-da7b76409dce"
},
"source": [
"from keras.models import Model\n",
"from keras.layers import Dense, Dropout, Activation, Flatten\n",
"from keras.layers import concatenate, Conv2D, MaxPooling2D, Conv2DTranspose\n",
"from keras.layers import Input, merge, UpSampling2D,BatchNormalization\n",
"from keras.callbacks import ModelCheckpoint\n",
"from keras.optimizers import Adam\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from keras import backend as K\n",
"import tensorflow as tf\n",
"\n",
"K.set_image_data_format('channels_first')\n",
"\n",
"\n",
"def dice_coef(y_true, y_pred):\n",
" smooth = 0.005 \n",
" y_true_f = K.flatten(y_true)\n",
" y_pred_f = K.flatten(y_pred)\n",
" intersection = K.sum(y_true_f * y_pred_f)\n",
" return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
"\n",
"\n",
"def dice_coef_loss(y_true, y_pred):\n",
" return 1-dice_coef(y_true, y_pred)\n",
" \n",
"def unet_model():\n",
" \n",
" inputs = Input((2, 240 , 240))\n",
" \n",
" conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)\n",
" batch1 = BatchNormalization(axis=1)(conv1)\n",
" conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)\n",
" batch1 = BatchNormalization(axis=1)(conv1)\n",
" pool1 = MaxPooling2D((2, 2)) (batch1)\n",
" \n",
" conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)\n",
" batch2 = BatchNormalization(axis=1)(conv2)\n",
" conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)\n",
" batch2 = BatchNormalization(axis=1)(conv2)\n",
" pool2 = MaxPooling2D((2, 2)) (batch2)\n",
" \n",
" conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)\n",
" batch3 = BatchNormalization(axis=1)(conv3)\n",
" conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)\n",
" batch3 = BatchNormalization(axis=1)(conv3)\n",
" pool3 = MaxPooling2D((2, 2)) (batch3)\n",
" \n",
" conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)\n",
" batch4 = BatchNormalization(axis=1)(conv4)\n",
" conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch4)\n",
" batch4 = BatchNormalization(axis=1)(conv4)\n",
" pool4 = MaxPooling2D(pool_size=(2, 2)) (batch4)\n",
" \n",
" conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (pool4)\n",
" batch5 = BatchNormalization(axis=1)(conv5)\n",
" conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (batch5)\n",
" batch5 = BatchNormalization(axis=1)(conv5)\n",
" \n",
" up6 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same') (batch5)\n",
" up6 = concatenate([up6, conv4], axis=1)\n",
" conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (up6)\n",
" batch6 = BatchNormalization(axis=1)(conv6)\n",
" conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch6)\n",
" batch6 = BatchNormalization(axis=1)(conv6)\n",
" \n",
" up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch6)\n",
" up7 = concatenate([up7, conv3], axis=1)\n",
" conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)\n",
" batch7 = BatchNormalization(axis=1)(conv7)\n",
" conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)\n",
" batch7 = BatchNormalization(axis=1)(conv7)\n",
" \n",
" up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)\n",
" up8 = concatenate([up8, conv2], axis=1)\n",
" conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)\n",
" batch8 = BatchNormalization(axis=1)(conv8)\n",
" conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)\n",
" batch8 = BatchNormalization(axis=1)(conv8)\n",
" \n",
" up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)\n",
" up9 = concatenate([up9, conv1], axis=1)\n",
" conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)\n",
" batch9 = BatchNormalization(axis=1)(conv9)\n",
" conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)\n",
" batch9 = BatchNormalization(axis=1)(conv9)\n",
"\n",
" conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)\n",
"\n",
" model = Model(inputs=[inputs], outputs=[conv10])\n",
"\n",
" model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])\n",
"\n",
" return model\n",
"\n",
"model = unet_model()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SzynFhVqvTjb",
"outputId": "f4ec3435-5dc8-47b9-9dab-46481cdd6a6e"
},
"source": [
"model.fit(x_train,seg,validation_split=0.30,batch_size=24\n",
" ,epochs=30,shuffle=True,verbose=1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/30\n",
"82/82 [==============================] - 276s 3s/step - loss: 0.8414 - dice_coef: 0.1587 - val_loss: 0.9501 - val_dice_coef: 0.0486\n",
"Epoch 2/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.7640 - dice_coef: 0.2365 - val_loss: 0.9494 - val_dice_coef: 0.0493\n",
"Epoch 3/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.7200 - dice_coef: 0.2801 - val_loss: 0.9375 - val_dice_coef: 0.0609\n",
"Epoch 4/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.6771 - dice_coef: 0.3233 - val_loss: 0.9042 - val_dice_coef: 0.0933\n",
"Epoch 5/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.6358 - dice_coef: 0.3648 - val_loss: 0.8638 - val_dice_coef: 0.1326\n",
"Epoch 6/30\n",
"82/82 [==============================] - 218s 3s/step - loss: 0.5892 - dice_coef: 0.4108 - val_loss: 0.8215 - val_dice_coef: 0.1737\n",
"Epoch 7/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.5418 - dice_coef: 0.4573 - val_loss: 0.7377 - val_dice_coef: 0.2553\n",
"Epoch 8/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.4820 - dice_coef: 0.5184 - val_loss: 0.5083 - val_dice_coef: 0.4787\n",
"Epoch 9/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.4211 - dice_coef: 0.5788 - val_loss: 0.4526 - val_dice_coef: 0.5328\n",
"Epoch 10/30\n",
"82/82 [==============================] - 232s 3s/step - loss: 0.3668 - dice_coef: 0.6327 - val_loss: 0.3800 - val_dice_coef: 0.6035\n",
"Epoch 11/30\n",
"82/82 [==============================] - 217s 3s/step - loss: 0.3119 - dice_coef: 0.6880 - val_loss: 0.4179 - val_dice_coef: 0.5667\n",
"Epoch 12/30\n",
"82/82 [==============================] - 232s 3s/step - loss: 0.2640 - dice_coef: 0.7363 - val_loss: 0.3391 - val_dice_coef: 0.6433\n",
"Epoch 13/30\n",
"82/82 [==============================] - 218s 3s/step - loss: 0.2266 - dice_coef: 0.7739 - val_loss: 0.3271 - val_dice_coef: 0.6550\n",
"Epoch 14/30\n",
"82/82 [==============================] - 217s 3s/step - loss: 0.1892 - dice_coef: 0.8110 - val_loss: 0.3370 - val_dice_coef: 0.6454\n",
"Epoch 15/30\n",
"82/82 [==============================] - 216s 3s/step - loss: 0.1655 - dice_coef: 0.8345 - val_loss: 0.3634 - val_dice_coef: 0.6197\n",
"Epoch 16/30\n",
"82/82 [==============================] - 217s 3s/step - loss: 0.1451 - dice_coef: 0.8548 - val_loss: 0.3392 - val_dice_coef: 0.6432\n",
"Epoch 17/30\n",
"82/82 [==============================] - 217s 3s/step - loss: 0.1277 - dice_coef: 0.8725 - val_loss: 0.3344 - val_dice_coef: 0.6478\n",
"Epoch 18/30\n",
"82/82 [==============================] - 218s 3s/step - loss: 0.1148 - dice_coef: 0.8853 - val_loss: 0.3324 - val_dice_coef: 0.6498\n",
"Epoch 19/30\n",
"82/82 [==============================] - 232s 3s/step - loss: 0.1074 - dice_coef: 0.8927 - val_loss: 0.3368 - val_dice_coef: 0.6456\n",
"Epoch 20/30\n",
"82/82 [==============================] - 218s 3s/step - loss: 0.1045 - dice_coef: 0.8957 - val_loss: 0.3003 - val_dice_coef: 0.6811\n",
"Epoch 21/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.0913 - dice_coef: 0.9089 - val_loss: 0.3013 - val_dice_coef: 0.6801\n",
"Epoch 22/30\n",
"82/82 [==============================] - 218s 3s/step - loss: 0.0872 - dice_coef: 0.9129 - val_loss: 0.3151 - val_dice_coef: 0.6667\n",
"Epoch 23/30\n",
"82/82 [==============================] - 218s 3s/step - loss: 0.0802 - dice_coef: 0.9198 - val_loss: 0.3088 - val_dice_coef: 0.6728\n",
"Epoch 24/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.0749 - dice_coef: 0.9252 - val_loss: 0.3156 - val_dice_coef: 0.6662\n",
"Epoch 25/30\n",
"82/82 [==============================] - 233s 3s/step - loss: 0.0748 - dice_coef: 0.9253 - val_loss: 0.3157 - val_dice_coef: 0.6661\n",
"Epoch 26/30\n",
"82/82 [==============================] - 217s 3s/step - loss: 0.0718 - dice_coef: 0.9280 - val_loss: 0.3243 - val_dice_coef: 0.6577\n",
"Epoch 27/30\n",
"82/82 [==============================] - 217s 3s/step - loss: 0.0697 - dice_coef: 0.9301 - val_loss: 0.2989 - val_dice_coef: 0.6825\n",
"Epoch 28/30\n",
"82/82 [==============================] - 232s 3s/step - loss: 0.0662 - dice_coef: 0.9337 - val_loss: 0.3120 - val_dice_coef: 0.6697\n",
"Epoch 29/30\n",
"82/82 [==============================] - 217s 3s/step - loss: 0.0610 - dice_coef: 0.9391 - val_loss: 0.3084 - val_dice_coef: 0.6732\n",
"Epoch 30/30\n",
"82/82 [==============================] - 232s 3s/step - loss: 0.0600 - dice_coef: 0.9401 - val_loss: 0.3075 - val_dice_coef: 0.6740\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7ff7b7e2fed0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "D05PwceH9ioH"
},
"source": [
"model.save_weights('/content/drive/MyDrive/brats-30epochs-batch24.h5')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2J-cve6APTBZ"
},
"source": [
"model.load_weights('/content/drive/MyDrive/brats-30epochs-batch24.h5')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "f1maLifgwRML",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235,
"referenced_widgets": [
"4d1240b0ca02405da2422d856b3d9fdc",
"de5798945bc64d4787678c0c43e4be65",
"d1c858e758be4f678e0a8c49f78d54cd",
"858ae36b92cf43c98d9613fe0b77f3b1",
"47aeb5d7248e4401b520d7452794acda",
"261aa94c5ed74413a6d52f01de405a8f",
"bb5e6c85b91941db9b4f03bec6230763",
"12655ee379624c98a490ae73665d6e67",
"e1f776d5144444589cb8d6bf67e1c935",
"6f38a8d96bc248efb071b604f353306f",
"b8132ecd468048b6846f20b9a779cda9",
"8824e1c6d11c457baee8017be7c7434e",
"431506fcc1324c52a9c12471d7484419",
"ab08d723d1c845919b35d7c14cab259b",
"40d01d31931840ab90c3d5767f003e9c",
"dc4b8924f3fa4225a2b9080b0475051e",
"7898a553f1a94afc85feae963867a8a8",
"40c862cddf054fa186d9f4928cce19d4",
"92e5de3800544cebb5e44a1ea7aa9a17",
"759351264d4345cab84fe12726e6d351",
"d48c154665484203988c63dd7eed802e",
"a61a80d6f3344804bb798894e0365977",
"902ca0538bd7441c98a8ff826b06e0b2",
"2de6b8e194974f25871601b9d3f63661"
]
},
"outputId": "d80c4f81-e58e-4595-e62a-8c448af42f52"
},
"source": [
"yol = '/content/MICCAI_BraTS2020_TrainingData/**/'\n",
"seg_geniş = seg_array(yol,'**/*seg.nii',4)\n",
"seg_ödemsiz = seg_array(yol,'**/*seg.nii',3)\n",
"seg_nekroz = seg_array(yol, '**/*seg.nii',2)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
" del sys.path[0]\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4d1240b0ca02405da2422d856b3d9fdc",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e1f776d5144444589cb8d6bf67e1c935",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7898a553f1a94afc85feae963867a8a8",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "enDALRRtwkKi",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 120,
"referenced_widgets": [
"c1d0196531c940f0b804daa09ac553a4",
"79c0a09e21684de99ec33a5d78af54a3",
"64fe467ae74e4743a4839396568974b1",
"bee6572f337541f28a8941ad9c915e98",
"d3d88737dbd345988d8e6a3da087d846",
"915eeb9b4f2e430abe90bacbdcbae4d3",
"6861a930539347958e45a6cfe6bf3853",
"0ce3442dd8e84d47b99d0d1b0a8378f5"
]
},
"outputId": "9fbb5f7e-2bbd-4029-90a4-ecd436d04182"
},
"source": [
"t1ce = train_array(yol,'**/*t1ce.nii')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c1d0196531c940f0b804daa09ac553a4",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_1HC7FGewkE_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "53df18b8-1ac3-48b6-9fd9-64d5880b205c"
},
"source": [
"seg_geniş.shape, seg_ödemsiz.shape, t1ce.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((2800, 1, 240, 240), (2800, 1, 240, 240), (2800, 1, 240, 240))"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ERtQ6B0gwuh5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 241
},
"outputId": "086dcca6-e8a9-44cc-852a-7cb86ac74b84"
},
"source": [
"plt.figure(figsize=(15,10))\n",
"\n",
"plt.subplot(3,4,1)\n",
"plt.title('T1ce')\n",
"plt.imshow(t1ce[425,0,:,:])\n",
"\n",
"plt.subplot(3,4,2)\n",
"plt.title('geniş')\n",
"plt.imshow(seg_geniş[425,0,:,:])\n",
"\n",
"plt.subplot(3,4,3)\n",
"plt.title('ödemsiz')\n",
"plt.imshow(seg_ödemsiz[425,0,:,:])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f836e0db190>"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 3 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uxEAuf_GBLfK",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f793c79d-3b5c-4fb4-f1aa-f86cff25ed1f"
},
"source": [
"ornek = np.expand_dims(x_train[460], axis=0)\n",
"ornek.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1, 2, 240, 240)"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4M6KKO8nBLhc"
},
"source": [
"pred = model.predict(ornek)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YIjuqTDeB4mi",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
},
"outputId": "ed38c47e-7d6a-434b-99e1-cf5fafad0538"
},
"source": [
"plt.imshow(seg[460][0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f834d0fc6d0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "spIxq9bQBLc_",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
},
"outputId": "7b92cd77-29f8-459d-a1a5-b704c932773a"
},
"source": [
"plt.imshow(pred[0][0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f834d1b8c90>"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EbRF5zNiEJ6S"
},
"source": [
"def show_prediction(count_index, color_index):\n",
" x = count_index\n",
"\n",
" color = {\n",
" 0:'magma',\n",
" 1:'viridis',\n",
" 2:'gray',\n",
" 3:'inferno',\n",
" 4:'cividis',\n",
" 5:'hot',\n",
" }\n",
"\n",
" a = color_index\n",
"\n",
" exam = np.expand_dims(x_train[x], axis=0)\n",
" pred = model.predict(exam)\n",
"\n",
" fig = plt.figure(figsize=(15,10))\n",
"\n",
" plt.subplot(141)\n",
" plt.title('Input (Flair + T2)')\n",
" plt.axis('off')\n",
" plt.imshow(x_train[x][0], cmap=color[a])\n",
"\n",
" plt.subplot(142)\n",
" plt.title('Radiologist (segmentation)')\n",
" plt.axis('off')\n",
" plt.imshow(seg[x][0], cmap=color[a])\n",
"\n",
" plt.subplot(143)\n",
" plt.title('Tahmin (prediction)')\n",
" plt.axis('off')\n",
" plt.imshow(pred[0][0], cmap=color[a])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-xe4j5rdEdZX",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 229
},
"outputId": "c94ce0a3-2587-4e87-b5f1-7e0f99d4e246"
},
"source": [
"show_prediction(1132, 5)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 3 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vj1y0V0RYS7M"
},
"source": [
"## KIRPMA ISLEMI"
]
},
{
"cell_type": "code",
"metadata": {
"id": "X9Y0tNqBeXIW",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
},
"outputId": "b1cdf9d2-a86e-4526-b0f2-582c38c31b55"
},
"source": [
"tmp = seg_ödemsiz[167, 0, :, :]\n",
"tmp.shape\n",
"plt.imshow(tmp)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f834d2c4190>"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sAAdGmxcStjU"
},
"source": [
"tmp[tmp > 0.2] = 1\n",
"tmp[tmp != 1] = 0"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "eemQX6f9Sirf"
},
"source": [
"index_xy = np.where(tmp==1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hnlUM-KqSip5",
"outputId": "01927385-19cf-4554-8e12-98805679e7ac"
},
"source": [
"index_xy"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([156, 156, 156, 156, 156, 157, 157, 157, 157, 157, 157, 157, 158,\n",
" 158, 158, 158, 158, 158, 158, 158, 159, 159, 159, 159, 159, 159,\n",
" 159, 159, 159, 160, 160, 160, 160, 160, 160, 160, 160, 161, 161,\n",
" 161, 161, 161, 161, 161, 161, 162, 162, 162, 162, 163, 163, 163,\n",
" 163, 164, 164, 164]),\n",
" array([101, 102, 103, 104, 105, 97, 100, 101, 102, 103, 104, 105, 97,\n",
" 98, 99, 100, 101, 102, 103, 104, 97, 98, 99, 100, 101, 102,\n",
" 103, 104, 105, 98, 99, 100, 101, 102, 103, 104, 105, 98, 99,\n",
" 100, 101, 102, 103, 104, 105, 99, 100, 101, 102, 100, 101, 102,\n",
" 103, 100, 101, 103]))"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DGun05QTSinw",
"outputId": "f1d5066c-d66b-4168-f00f-158adca446b4"
},
"source": [
"merkez_y = (max(index_xy[0]) + (min(index_xy[0])) ) / 2\n",
"merkez_y"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"160.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-0oEPRD2Siin",
"outputId": "3027ffa2-706c-4181-8663-7378742e14e9"
},
"source": [
"merkez_x = (max(index_xy[1]) + (min(index_xy[1])) ) / 2\n",
"merkez_x"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"101.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ef_aMJN9VfMj",
"outputId": "a2cf930c-73b1-478f-d70f-3c61d2354ad9"
},
"source": [
"# u net sadece 4 un katlarini kabul ediyor\n",
"\n",
"img_x = np.zeros((64,64), np.float32)\n",
"img_x"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cQExfi05VfKC",
"outputId": "e7ff82ff-0f1e-4f18-a0d5-392912d8a070"
},
"source": [
"x = t1ce[167, 0, :, :]\n",
"x.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(240, 240)"
]
},
"metadata": {
"tags": []
},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LX7dFuRGVfHJ",
"outputId": "f8f672a4-a887-4dd6-c3ec-e7e6b2d64808"
},
"source": [
"ornek = x[60:80, 60:70]\n",
"ornek.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(20, 10)"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
},
"id": "1apGaL2jVfCc",
"outputId": "34a164be-cb68-459c-c936-8d0f869cd5fc"
},
"source": [
"img_x[:, :] = x[int(merkez_y - 64 / 2):int(merkez_y + 64 / 2), int(merkez_x - 64 / 2):int(merkez_x + 64 / 2)]\n",
"plt.imshow(img_x)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f834d293550>"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LVNQyqmuYGgf",
"outputId": "5b19e378-0aef-4b08-b253-1e6305fb74a9"
},
"source": [
"seg_ = seg_ödemsiz[167,0,:,:]\n",
"seg_.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(240, 240)"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
},
"id": "HNBkel1MX88W",
"outputId": "e36c03cb-f647-41f0-f261-d4b906bf0ab8"
},
"source": [
"img_x[:, :] = seg_[int(merkez_y - 64 / 2):int(merkez_y + 64 / 2), int(merkez_x - 64 / 2):int(merkez_x + 64 / 2)]\n",
"plt.imshow(img_x)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f835e055790>"
]
},
"metadata": {
"tags": []
},
"execution_count": 34
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SsbLBtABYPGU",
"outputId": "7ae14a6d-d17a-4619-ce07-fbf193182f1a"
},
"source": [
"img_x.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(64, 64)"
]
},
"metadata": {
"tags": []
},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9TzKE9_SaTi7",
"outputId": "0d696d83-9680-45dc-8a2b-7eb305ec9503"
},
"source": [
"tmp"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GjbSad88YbAe"
},
"source": [
"def tümör_kırpma(mr,seg):\n",
" \n",
" mr = mr[0]\n",
" liste = []\n",
" tmp = seg[0,:,:]\n",
" tmp[tmp>0.2] = 1\n",
" tmp[tmp!= 1] = 0\n",
" index_xy = np.where(tmp==1)\n",
" \n",
" if index_xy[0] != []:\n",
" merkez_y = (max(index_xy[0]) + (min(index_xy[0])) ) / 2\n",
" merkez_x = (max(index_xy[1]) + (min(index_xy[1])) ) / 2\n",
" img_x = np.zeros((64,64), np.float32)\n",
" img_x[:,:] = mr[int(merkez_y - 64/2):int(merkez_y + 64/2),int(merkez_x - 64/2):int(merkez_x + 64/2) ]\n",
" liste.append(img_x)\n",
" \n",
" return np.array(liste)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jSzGBVDTYa-c",
"outputId": "11a95a73-dfa4-4531-b62c-00434c63eb5f"
},
"source": [
"tice_def = tümör_kırpma(t1ce[167], seg_ödemsiz[167])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
},
"id": "D_I9MRlVYa8t",
"outputId": "04d9edf9-9c91-4c5e-f412-02ba976d382b"
},
"source": [
"plt.imshow(tice_def[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f835e248f50>"
]
},
"metadata": {
"tags": []
},
"execution_count": 39
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PsplGnFeYa6U",
"outputId": "7067222d-a4be-4e6f-e55c-3e1b6a922b1e"
},
"source": [
"t1ce_def = tümör_kırpma(t1ce[167], seg_ödemsiz[167])\n",
"nekroz_def = tümör_kırpma(seg_nekroz[167], seg_ödemsiz[167])\n",
"geniş_def = tümör_kırpma(seg_geniş[167], seg_ödemsiz[167])\n",
"ödemsiz = tümör_kırpma(seg_ödemsiz[167], seg_ödemsiz[167])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "PnEK0VVAYa2e",
"outputId": "b9f475e5-7dcc-4a2d-f80d-df5198ac2008"
},
"source": [
"plt.figure(figsize=(15, 10))\n",
"\n",
"plt.subplot(3, 4, 1)\n",
"plt.title(\"Tam Tümor\")\n",
"plt.imshow(t1ce_def[0])\n",
"\n",
"plt.subplot(3, 4, 2)\n",
"plt.title(\"Nekroz\")\n",
"plt.imshow(nekroz_def[0])\n",
"\n",
"plt.subplot(3, 4, 3)\n",
"plt.title(\"Genişleyen\")\n",
"plt.imshow(geniş_def[0])\n",
"\n",
"plt.subplot(3, 4, 4)\n",
"plt.title(\"Nekroz + Genişleyen\")\n",
"plt.imshow(ödemsiz[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f834d7fcd50>"
]
},
"metadata": {
"tags": []
},
"execution_count": 41
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 4 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GI0pmrIycjlg",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f1418970-399a-4984-887f-a9c3635abf23"
},
"source": [
"liste_ = []\n",
"for i in range(len(t1ce)):\n",
" img = tümör_kırpma(t1ce[i], seg_ödemsiz[i])\n",
" if len(img.shape) > 2: # (0,) (160, 160)\n",
" liste_.append(img)\n",
" print(f'{i}')\n",
"\n",
"train = np.array(liste_)\n",
"train.shape "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
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],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1062, 1, 64, 64)"
]
},
"metadata": {
"tags": []
},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "teeI6vml6nxK",
"outputId": "8e2dbdbc-d4c6-4014-886e-4f5d7a85a32e"
},
"source": [
"liste_2 = []\n",
"\n",
"for i in range(len(t1ce)):\n",
" img = tümör_kırpma(seg_nekroz[i], seg_ödemsiz[i])\n",
" if len(img.shape) > 2: # (0,) (160, 160)\n",
" liste_2.append(img)\n",
" print(f'{i}')\n",
"\n",
"train_y = np.array(liste_2)\n",
"train_y.shape "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
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"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
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],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1062, 1, 64, 64)"
]
},
"metadata": {
"tags": []
},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
},
"id": "cDvVuJUz5fpp",
"outputId": "e505a7e1-ebd0-474e-f165-5262e0036580"
},
"source": [
"plt.imshow(train[6,0,:,:])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f834cfed310>"
]
},
"metadata": {
"tags": []
},
"execution_count": 44
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
},
"id": "edW47xr95flK",
"outputId": "c327ad25-ed12-4d89-d0e3-4e9b67882e27"
},
"source": [
"plt.imshow(train_y[6,0,:,:])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f834d33a7d0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 45
},
{
"output_type": "display_data",
"data": {
"image/png": 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Qne7ycklXr0vYxv65C/i0pDPAXjpd+cf7UAcRca74PgE8R+cPYNv7pafLts+mzbC/BKwvjrQuBj5L53LU/dL6pbAlic5ttE5ExNf6VYukGyUtLx6/j85xgxN0Qn9/W3VExO6IWBMRa+n8PvxzRHy+7TokXS/pA1cfA58EjtLyfommL9ve9IGPSQca7gPeoDM+/MsWt/tN4Dxwmc5fzx10xoZjwEngn4AVLdRxN50u2GvAkeLrvrZrAT4KvFLUcRT4q2L97wAvAqeAbwFLWtxH9wD7+1FHsb1Xi69jV383+/Q7shEYL/bNPwI31FWHz6AzS8IH6MyScNjNknDYzZJw2M2ScNjNknDYzZJw2M2ScNjNkvhferFZHILYalEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "62yWwrbe5fJZ",
"outputId": "d4662dab-abcf-496c-b53e-b92e94b2c5a2"
},
"source": [
"liste_1 = []\n",
"for i in range(len(t1ce)):\n",
" img = tümör_kırpma(t1ce[i], seg_ödemsiz[i])\n",
" if len(img.shape) > 2:\n",
" liste_1.append(img)\n",
" print(len(liste_1)) "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
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"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y7S7BsVx88NM",
"outputId": "720e1cce-c796-4e61-8bbe-c9e92591bd08"
},
"source": [
"liste_2 = []\n",
"for i in range(len(t1ce)):\n",
" img = tümör_kırpma(seg_nekroz[i], seg_ödemsiz[i])\n",
" if len(img.shape) > 2:\n",
" liste_2.append(img)\n",
" print(len(liste_2)) "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
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"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
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"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RDNSX3ie88zt",
"outputId": "40d01721-dfa2-4298-c032-5bc2aa84b1bb"
},
"source": [
"liste_3 = []\n",
"for i in range(len(t1ce)):\n",
" img = tümör_kırpma(seg_geniş[i], seg_ödemsiz[i])\n",
" if len(img.shape) > 2:\n",
" liste_3.append(img)\n",
" print(len(liste_3)) "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
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"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
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"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NW0y-b7y89WL",
"outputId": "50a377bb-9a78-4ed3-8e4e-fedef6dfc6ec"
},
"source": [
"liste_4 = []\n",
"for i in range(len(t1ce)):\n",
" img = tümör_kırpma(seg_ödemsiz[i], seg_ödemsiz[i])\n",
" if len(img.shape) > 2:\n",
" liste_4.append(img)\n",
" print(len(liste_4)) "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
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"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" # Remove the CWD from sys.path while we load stuff.\n"
],
"name": "stderr"
},
{
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"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YcU2VzZD9SD9"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UNPcNFA294-t"
},
"source": [
"t1ce_ = np.array(liste_1)\n",
"odemsiz_ = np.array(liste_4)\n",
"nekroz_ = np.array(liste_2)\n",
"genis_ = np.array(liste_3)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ivScbnrbDVL4",
"outputId": "31de17e0-4043-430a-d0c5-b80c753351c1"
},
"source": [
"t1ce_.shape, odemsiz_.shape, nekroz_.shape, genis_.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((1062, 1, 64, 64), (1062, 1, 64, 64), (1062, 1, 64, 64), (1062, 1, 64, 64))"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QiG9AdXTDhy7",
"outputId": "3475c8f3-e5ec-4084-affe-f33b527c2d65"
},
"source": [
"\n",
"def unet_model_7():\n",
" \n",
" inputs = Input((1, 64 , 64))\n",
" \n",
" conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)\n",
" batch1 = BatchNormalization(axis=1)(conv1)\n",
" conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)\n",
" batch1 = BatchNormalization(axis=1)(conv1)\n",
" pool1 = MaxPooling2D((2, 2)) (batch1)\n",
" \n",
" conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)\n",
" batch2 = BatchNormalization(axis=1)(conv2)\n",
" conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)\n",
" batch2 = BatchNormalization(axis=1)(conv2)\n",
" pool2 = MaxPooling2D((2, 2)) (batch2)\n",
" \n",
" conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)\n",
" batch3 = BatchNormalization(axis=1)(conv3)\n",
" conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)\n",
" batch3 = BatchNormalization(axis=1)(conv3)\n",
" pool3 = MaxPooling2D((2, 2)) (batch3)\n",
" \n",
" conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (pool3)\n",
" batch5 = BatchNormalization(axis=1)(conv5)\n",
" conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (batch5)\n",
" batch5 = BatchNormalization(axis=1)(conv5)\n",
" \n",
" up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch5)\n",
" up7 = concatenate([up7, conv3], axis=1)\n",
" conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)\n",
" batch7 = BatchNormalization(axis=1)(conv7)\n",
" conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)\n",
" batch7 = BatchNormalization(axis=1)(conv7)\n",
" \n",
" up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)\n",
" up8 = concatenate([up8, conv2], axis=1)\n",
" conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)\n",
" batch8 = BatchNormalization(axis=1)(conv8)\n",
" conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)\n",
" batch8 = BatchNormalization(axis=1)(conv8)\n",
" \n",
" up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)\n",
" up9 = concatenate([up9, conv1], axis=1)\n",
" conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)\n",
" batch9 = BatchNormalization(axis=1)(conv9)\n",
" conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)\n",
" batch9 = BatchNormalization(axis=1)(conv9)\n",
"\n",
" conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)\n",
"\n",
" model = Model(inputs=[inputs], outputs=[conv10])\n",
"\n",
" model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])\n",
"\n",
" return model\n",
"\n",
"model = unet_model()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6dihgtI-EO6m",
"outputId": "eaf6bc36-314c-4c58-f239-2f3af323e348"
},
"source": [
"model_odemsiz = unet_model_7()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dKumxWvqEO4O",
"outputId": "f8732de1-aa65-4146-90e8-b425abfd1eb6"
},
"source": [
"history = model_odemsiz.fit(\n",
" t1ce_, odemsiz_,\n",
" validation_split=0.10,\n",
" batch_size = 10,\n",
" epochs = 20,\n",
" shuffle=True\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"96/96 [==============================] - 20s 162ms/step - loss: 0.4675 - dice_coef: 0.5325 - val_loss: 0.9132 - val_dice_coef: 0.0861\n",
"Epoch 2/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.1975 - dice_coef: 0.8025 - val_loss: 0.9852 - val_dice_coef: 0.0147\n",
"Epoch 3/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.1349 - dice_coef: 0.8651 - val_loss: 0.6236 - val_dice_coef: 0.3663\n",
"Epoch 4/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.1068 - dice_coef: 0.8932 - val_loss: 0.2564 - val_dice_coef: 0.7295\n",
"Epoch 5/20\n",
"96/96 [==============================] - 11s 116ms/step - loss: 0.1045 - dice_coef: 0.8955 - val_loss: 0.1239 - val_dice_coef: 0.8765\n",
"Epoch 6/20\n",
"96/96 [==============================] - 11s 119ms/step - loss: 0.0857 - dice_coef: 0.9143 - val_loss: 0.0849 - val_dice_coef: 0.9150\n",
"Epoch 7/20\n",
"96/96 [==============================] - 11s 116ms/step - loss: 0.0783 - dice_coef: 0.9217 - val_loss: 0.0772 - val_dice_coef: 0.9230\n",
"Epoch 8/20\n",
"96/96 [==============================] - 11s 119ms/step - loss: 0.0673 - dice_coef: 0.9327 - val_loss: 0.0741 - val_dice_coef: 0.9261\n",
"Epoch 9/20\n",
"96/96 [==============================] - 11s 116ms/step - loss: 0.0585 - dice_coef: 0.9415 - val_loss: 0.0657 - val_dice_coef: 0.9344\n",
"Epoch 10/20\n",
"96/96 [==============================] - 12s 120ms/step - loss: 0.0529 - dice_coef: 0.9471 - val_loss: 0.0640 - val_dice_coef: 0.9363\n",
"Epoch 11/20\n",
"96/96 [==============================] - 11s 119ms/step - loss: 0.0474 - dice_coef: 0.9526 - val_loss: 0.0649 - val_dice_coef: 0.9353\n",
"Epoch 12/20\n",
"96/96 [==============================] - 11s 119ms/step - loss: 0.0511 - dice_coef: 0.9489 - val_loss: 0.0615 - val_dice_coef: 0.9388\n",
"Epoch 13/20\n",
"96/96 [==============================] - 11s 116ms/step - loss: 0.0558 - dice_coef: 0.9442 - val_loss: 0.0765 - val_dice_coef: 0.9241\n",
"Epoch 14/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.0500 - dice_coef: 0.9500 - val_loss: 0.0601 - val_dice_coef: 0.9399\n",
"Epoch 15/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.0418 - dice_coef: 0.9582 - val_loss: 0.0577 - val_dice_coef: 0.9425\n",
"Epoch 16/20\n",
"96/96 [==============================] - 11s 119ms/step - loss: 0.0390 - dice_coef: 0.9610 - val_loss: 0.0577 - val_dice_coef: 0.9426\n",
"Epoch 17/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.0466 - dice_coef: 0.9534 - val_loss: 0.0584 - val_dice_coef: 0.9419\n",
"Epoch 18/20\n",
"96/96 [==============================] - 11s 119ms/step - loss: 0.0393 - dice_coef: 0.9607 - val_loss: 0.0550 - val_dice_coef: 0.9452\n",
"Epoch 19/20\n",
"96/96 [==============================] - 11s 118ms/step - loss: 0.0348 - dice_coef: 0.9652 - val_loss: 0.0509 - val_dice_coef: 0.9492\n",
"Epoch 20/20\n",
"96/96 [==============================] - 11s 118ms/step - loss: 0.0355 - dice_coef: 0.9645 - val_loss: 0.0477 - val_dice_coef: 0.9524\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jAWxVamOEOvV"
},
"source": [
"model_odemsiz.save_weights('/content/drive/MyDrive/brats-nekroz-20epochs-batch10.h5')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "t8wlAPnmlAhc",
"outputId": "82e81b60-8834-407d-f924-b80bec4a6165"
},
"source": [
"model_genis = unet_model_7()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dnS3pdCulEkb",
"outputId": "205e31a6-6eeb-44dc-ab49-ada4ce5284a0"
},
"source": [
"history = model_genis.fit(\n",
" t1ce_, genis_,\n",
" validation_split=0.10,\n",
" batch_size = 10,\n",
" epochs = 20,\n",
" shuffle=True\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"96/96 [==============================] - 17s 133ms/step - loss: 0.6665 - dice_coef: 0.3335 - val_loss: 0.7690 - val_dice_coef: 0.2264\n",
"Epoch 2/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.4457 - dice_coef: 0.5543 - val_loss: 0.8448 - val_dice_coef: 0.1515\n",
"Epoch 3/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.3772 - dice_coef: 0.6228 - val_loss: 0.7051 - val_dice_coef: 0.2889\n",
"Epoch 4/20\n",
"96/96 [==============================] - 11s 120ms/step - loss: 0.3194 - dice_coef: 0.6805 - val_loss: 0.2445 - val_dice_coef: 0.7563\n",
"Epoch 5/20\n",
"96/96 [==============================] - 11s 120ms/step - loss: 0.2624 - dice_coef: 0.7376 - val_loss: 0.1881 - val_dice_coef: 0.8119\n",
"Epoch 6/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.2395 - dice_coef: 0.7605 - val_loss: 0.2170 - val_dice_coef: 0.7845\n",
"Epoch 7/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.2166 - dice_coef: 0.7834 - val_loss: 0.1687 - val_dice_coef: 0.8319\n",
"Epoch 8/20\n",
"96/96 [==============================] - 12s 121ms/step - loss: 0.1780 - dice_coef: 0.8220 - val_loss: 0.1521 - val_dice_coef: 0.8481\n",
"Epoch 9/20\n",
"96/96 [==============================] - 11s 119ms/step - loss: 0.1578 - dice_coef: 0.8422 - val_loss: 0.1503 - val_dice_coef: 0.8505\n",
"Epoch 10/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.1620 - dice_coef: 0.8380 - val_loss: 0.1395 - val_dice_coef: 0.8607\n",
"Epoch 11/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.1334 - dice_coef: 0.8666 - val_loss: 0.1349 - val_dice_coef: 0.8655\n",
"Epoch 12/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.1374 - dice_coef: 0.8626 - val_loss: 0.1377 - val_dice_coef: 0.8631\n",
"Epoch 13/20\n",
"96/96 [==============================] - 11s 118ms/step - loss: 0.1233 - dice_coef: 0.8767 - val_loss: 0.1425 - val_dice_coef: 0.8585\n",
"Epoch 14/20\n",
"96/96 [==============================] - 11s 118ms/step - loss: 0.1135 - dice_coef: 0.8865 - val_loss: 0.1242 - val_dice_coef: 0.8761\n",
"Epoch 15/20\n",
"96/96 [==============================] - 12s 120ms/step - loss: 0.1198 - dice_coef: 0.8802 - val_loss: 0.1174 - val_dice_coef: 0.8832\n",
"Epoch 16/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.1103 - dice_coef: 0.8897 - val_loss: 0.1224 - val_dice_coef: 0.8783\n",
"Epoch 17/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.0994 - dice_coef: 0.9006 - val_loss: 0.1220 - val_dice_coef: 0.8784\n",
"Epoch 18/20\n",
"96/96 [==============================] - 11s 118ms/step - loss: 0.0988 - dice_coef: 0.9012 - val_loss: 0.1143 - val_dice_coef: 0.8867\n",
"Epoch 19/20\n",
"96/96 [==============================] - 11s 117ms/step - loss: 0.0953 - dice_coef: 0.9047 - val_loss: 0.1100 - val_dice_coef: 0.8907\n",
"Epoch 20/20\n",
"96/96 [==============================] - 11s 120ms/step - loss: 0.0885 - dice_coef: 0.9115 - val_loss: 0.1078 - val_dice_coef: 0.8924\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3242CgydlKHs"
},
"source": [
"model_genis.save_weights('/content/drive/MyDrive/brats-genis-20epochs-batch10.h5')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "xayYd9dXZ3el"
},
"source": [
"model_genis.load_weights('/content/drive/MyDrive/brats-genis-20epochs-batch10.h5')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gMIwJCwrZ-NH"
},
"source": [
"model_odemsiz.load_weights('/content/drive/MyDrive/brats-nekroz-20epochs-batch10.h5')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "xBgbT7bnnZmv",
"outputId": "7d2d13b7-cddf-44ad-cd70-124f6ee0f819"
},
"source": [
"x = 155\n",
"a = 'viridis'\n",
"\n",
"plt.figure(figsize=(15,10))\n",
"\n",
"plt.subplot(3,4,1)\n",
"plt.title('T1ce')\n",
"plt.imshow(t1ce[x,0,:,:], cmap=a)\n",
"\n",
"plt.subplot(3,4,2)\n",
"plt.title('T1ce')\n",
"plt.imshow(t1ce_[x,0,:,:], cmap=a)\n",
"\n",
"pred_genis = model_genis.predict(t1ce_[x:x+1,:,:,:])\n",
"plt.subplot(3,4,3)\n",
"plt.title('Tahmin (Genişleyen)')\n",
"plt.imshow(pred_genis[0,0,:,:], cmap=a)\n",
"\n",
"plt.subplot(3,4,4)\n",
"plt.title('geniş')\n",
"plt.imshow(genis_[x,0,:,:], cmap=a)\n",
"\n",
"plt.subplot(3,4,5)\n",
"plt.title('ödemsiz')\n",
"plt.imshow(odemsiz_[x,0,:,:], cmap=a)\n",
"\n",
"pred_odemsiz = model_odemsiz.predict(t1ce_[x:x+1,:,:,:])\n",
"plt.subplot(3,4,6)\n",
"plt.title('Tahmin (Nekroz + Genişleyen)')\n",
"plt.imshow(pred_odemsiz[0,0,:,:], cmap=a)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f8180231c10>"
]
},
"metadata": {
"tags": []
},
"execution_count": 66
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 6 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ieyxe-6fnZi8"
},
"source": [
"def tümör_kırp(x, pred, size): \n",
" crop_x = []\n",
" list_xy = []\n",
" p_tmp = pred[0,:,:]\n",
" p_tmp[p_tmp>0.2] = 1 \n",
" p_tmp[p_tmp !=1] = 0\n",
" index_xy = np.where(p_tmp==1) \n",
"\n",
" if index_xy[0].shape[0] == 0: \n",
" return [],[]\n",
" \n",
" center_x = (max(index_xy[0]) + min(index_xy[0])) / 2 \n",
" center_y = (max(index_xy[1]) + min(index_xy[1])) / 2 \n",
" \n",
" if center_x >= 176:\n",
" center_x = center_x-8\n",
" \n",
" length = max(index_xy[0]) - min(index_xy[0])\n",
" width = max(index_xy[1]) - min(index_xy[1])\n",
" \n",
" if width <= 64 and length <= 64: #64x64\n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y - size/2) : int(center_y + size/2)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x - size/2),int(center_y - size/2)))\n",
" \n",
" if width > 64 and length <= 64: #64x128\n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y - size) : int(center_y)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x - size/2),int(center_y - size)))\n",
" \n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y + 1) : int(center_y + size + 1)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x - size/2),int(center_y)))\n",
" \n",
" if width <= 64 and length > 64: #128x64 \n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x - size) : int(center_x),int(center_y - size/2) : int(center_y + size/2)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x - size),int(center_y - size/2)))\n",
" \n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x + 1) : int(center_x + size + 1),int(center_y - size/2) : int(center_y + size/2)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x),int(center_y - size/2)))\n",
" \n",
" if width > 64 and length > 64: #128x128\n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x - size) : int(center_x),int(center_y - size) : int(center_y)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x - size),int(center_y - size)))\n",
" \n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x + 1) : int(center_x + size + 1),int(center_y - size) : int(center_y)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x),int(center_y - size)))\n",
" \n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x - size) : int(center_x),int(center_y + 1) : int(center_y + size + 1)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x - size),int(center_y)))\n",
" \n",
" img_x = np.zeros((1,size,size),np.float32)\n",
" img_x[:,:,:] = x[:,int(center_x + 1) : int(center_x + size + 1),int(center_y + 1) : int(center_y + size + 1)]\n",
" crop_x.append(img_x)\n",
" list_xy.append((int(center_x),int(center_y)))\n",
" \n",
" \n",
" \n",
" return np.array(crop_x) , list_xy"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lqLZ2uXTnZgk"
},
"source": [
"goruntu1, kordinat1 = tümör_kırp(t1ce[167,:,:,:], seg[167,:,:,:], 64)\n",
"goruntu2, kordinat2 = tümör_kırp(seg_geniş[167,:,:,:], seg[167,:,:,:], 64)\n",
"goruntu3, kordinat3 = tümör_kırp(seg_ödemsiz[167,:,:,:], seg[167,:,:,:], 64)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mEUNj3cEnZeV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f2ea4306-fac6-45ad-bc00-8e315b732004"
},
"source": [
"goruntu1.shape, goruntu2.shape, goruntu3.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((2, 1, 64, 64), (2, 1, 64, 64), (2, 1, 64, 64))"
]
},
"metadata": {
"tags": []
},
"execution_count": 78
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "c-SZpo8LnZb8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 236
},
"outputId": "17ea277a-f734-46a3-b018-d9b6f9faf7d3"
},
"source": [
"plt.figure(figsize=(15,10))\n",
"\n",
"plt.subplot(3,4,1)\n",
"plt.title('t1ce')\n",
"plt.imshow(t1ce[167,0,:,:])\n",
"\n",
"plt.subplot(3,4,2)\n",
"plt.title('t1ce kırpılmış (1. kırpma)')\n",
"plt.imshow(goruntu1[0,0,:,:])\n",
"\n",
"plt.subplot(3,4,3)\n",
"plt.title('genişleyen tümör')\n",
"plt.imshow(goruntu2[0,0,:,:])\n",
"\n",
"plt.subplot(3,4,4)\n",
"plt.title('genişleyen + nekroz')\n",
"plt.imshow(goruntu3[0,0,:,:])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f8179478bd0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 79
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 4 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TPls9Jq_nwEB"
},
"source": [
"görüntü , koordinat = tümör_kırp(t1ce[167,:,:,:],seg[167,:,:,:],64)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dWZpKdBjno03"
},
"source": [
"pred_ödemsiz = model_odemsiz.predict(görüntü)\n",
"pred_geniş = model_genis.predict(görüntü)\n",
"pred_tam = model.predict(x_train[167:168,:,:,:])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jO4LAGrXnosc"
},
"source": [
"pred_tam[pred_tam > 0.2] = 2\n",
"pred_tam[pred_tam != 2 ] = 0\n",
"\n",
"pred_ödemsiz[pred_ödemsiz > 0.2] = 1\n",
"pred_ödemsiz[pred_ödemsiz != 1 ] = 0\n",
"\n",
"pred_geniş[pred_geniş > 0.2] = 4\n",
"pred_geniş[pred_geniş != 4 ] = 0"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ban4Arm9nZZl"
},
"source": [
"def üstüne_ekle(pred_tam, pred_ödemsiz , pred_geniş , koordinat): \n",
" \n",
" total = np.zeros((1,240,240),np.float32) \n",
" total[:,:,:] = pred_tam[:,:,:]\n",
" \n",
" for i in range(pred_ödemsiz.shape[0]):\n",
" for j in range(64):\n",
" for k in range(64):\n",
" \n",
" if pred_ödemsiz[i,0,j,k] != 0 and pred_tam[0,koordinat[i][0]+j,koordinat[i][1]+k] !=0:\n",
" total[0,koordinat[i][0]+j,koordinat[i][1]+k] = pred_ödemsiz[i,0,j,k]\n",
" \n",
" if pred_geniş[i,0,j,k] != 0 and pred_tam[0,koordinat[i][0]+j,koordinat[i][1]+k] !=0:\n",
" total[0,koordinat[i][0]+j,koordinat[i][1]+k] = pred_geniş[i,0,j,k]\n",
" \n",
" return total"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "AV8mPO9LnI31"
},
"source": [
"deneme = üstüne_ekle(pred_tam[0,:,:,:], pred_ödemsiz, pred_geniş, koordinat)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
},
"id": "PWwjb3mBnI1u",
"outputId": "ace99eee-1778-4ae6-b108-d8bce48ad3b9"
},
"source": [
"plt.imshow(deneme[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f81789aa4d0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 98
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XYyzCyVYnIz2"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OcUg5I8bnIwA"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8eZly76UnIsV"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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