V-Net in Keras and tensorflow

Keras.ipynb

Load data.ipynb

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   "source": [
    "from os import listdir\n",
    "import SimpleITK\n",
    "import os.path\n",
    "import pickle\n",
    "import numpy"
   ]
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "volSize = numpy.array((128,128,64), numpy.int32)\n",
    "dstRes  = numpy.array((1,1,1.5))\n",
    "normDir = False\n",
    "method  = SimpleITK.sitkLinear"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "def process_scan(scan):\n",
    "    ret = numpy.zeros(volSize, dtype=numpy.float32)\n",
    "    factor = numpy.asarray(scan.GetSpacing()) / dstRes\n",
    "\n",
    "    factorSize = numpy.asarray(scan.GetSize() * factor, dtype=numpy.float)\n",
    "\n",
    "    newSize = numpy.max([factorSize, volSize], axis=0)\n",
    "\n",
    "    newSize = newSize.astype(dtype=numpy.int32)\n",
    "\n",
    "    T=SimpleITK.AffineTransform(3)\n",
    "    T.SetMatrix(scan.GetDirection())\n",
    "\n",
    "    resampler = SimpleITK.ResampleImageFilter()\n",
    "    resampler.SetReferenceImage(scan)\n",
    "    resampler.SetOutputSpacing(dstRes)\n",
    "    resampler.SetSize(newSize.tolist())\n",
    "    resampler.SetInterpolator(method)\n",
    "    if normDir:\n",
    "        resampler.SetTransform(T.GetInverse())\n",
    "\n",
    "    imgResampled = resampler.Execute(scan)\n",
    "\n",
    "\n",
    "    imgCentroid = numpy.asarray(newSize, dtype=numpy.float) / 2.0\n",
    "\n",
    "    imgStartPx = (imgCentroid - numpy.array(volSize) / 2.0).astype(dtype=int)\n",
    "\n",
    "    regionExtractor = SimpleITK.RegionOfInterestImageFilter()\n",
    "    regionExtractor.SetSize(volSize.astype(dtype=numpy.int32).tolist())\n",
    "    regionExtractor.SetIndex(imgStartPx.tolist())\n",
    "\n",
    "    imgResampledCropped = regionExtractor.Execute(imgResampled)\n",
    "\n",
    "    return numpy.transpose(\n",
    "        SimpleITK.GetArrayFromImage(imgResampledCropped).astype(dtype=numpy.float),\n",
    "        [2, 1, 0]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "def iterate_folder(folder):\n",
    "    for filename in sorted(listdir(folder)):\n",
    "        absolute_filename = os.path.join(folder, filename)\n",
    "        segmentation_absolute_filename = absolute_filename[:-4] + '_segmentation.mhd'\n",
    "        if filename.endswith('.mhd') and os.path.exists(segmentation_absolute_filename):\n",
    "            yield absolute_filename, segmentation_absolute_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(folder):\n",
    "    input_filenames, label_filenames = zip(*list(iterate_folder(folder)))\n",
    "    \n",
    "    X = numpy.array([process_scan(SimpleITK.ReadImage(f)) for f in input_filenames])\n",
    "    y = numpy.array([process_scan(SimpleITK.ReadImage(f)) for f in label_filenames])\n",
    "    \n",
    "    return X, y > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, y = load_data('../data/PROMISE2012/train/')\n",
    "X.shape, y.shape, y.mean() \n",
    "\n",
    "with open('../data/PROMISE2012/train_data.p3', 'wb') as f:\n",
    "    pickle.dump([X, y], f)"
   ]
  }
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