mehrtash

 for file in ./*.pdf; 
 do 
 	gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.6 -dPDFSETTINGS=/ebook -dNOPAUSE -dQUIET -dBATCH -sOutputFile=output.pdf "$file"; 
 	mv -- output.pdf "$file";
 done;
 rm output.pdf

mehrtash

import time

import matplotlib.pyplot as plt
from scipy.signal import resample
import numpy as np
from sklearn.metrics import roc_auc_score, roc_curve

def bootstrap_auc(y_true, y_pred, ax, nsamples=1000):
    from scipy.interpolate import interp1d
    auc_values = []

mehrtash

fig, ax = plt.subplots(1,1, figsize=(4, 3), dpi=80)
ims = list()
for index, image_slice in enumerate(tqdm(X)):
    im = ax.imshow(image_slice[...,0], cmap=custom_colormap)
    ims.append([im])
im_ani = animation.ArtistAnimation(fig, ims, interval=200, repeat_delay=300, blit=True)
im_ani.save(os.path.join(selected_fold_folder, 'test.gif'), writer='imagemagick')

mehrtash

import numpy as np
import SimpleITK as sitk

def bounding_box(arr, padding=0, square=False):
    a = np.where(arr != 0)
    if a[0].size and a[1].size:
        min_ax0 = np.min(a[0])
        max_ax0 = np.max(a[0])
        min_ax1 = np.min(a[1])
        max_ax1 = np.max(a[1])

mehrtash

# load a prostate image, change the name to 1 and create an empty label map for it in editor, then paste the following code
# in python interactor.

import DeepInfer
import os
from collections import OrderedDict
import json

logic = DeepInfer.DeepInferLogic()
parameters = DeepInfer.ModelParameters()

mehrtash

FROM ubuntu:14.04
MAINTAINER Alireza Mehrtash <mehrtash@bwh.harvard.edu>

ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
ENV CONDA_DIR /opt/conda
ENV PATH $CONDA_DIR/bin:$PATH
RUN apt-get update --fix-missing && apt-get install -y wget bzip2 ca-certificates \
    libhdf5-dev g++ graphviz libglib2.0-0 libxext6 libsm6 libxrender1 \
    git mercurial subversion

mehrtash

#directory = 'G:/01-Work/QIICR/DCE BAT project/QIBA/QIBA_v9_Tofts/QIBA_v9_Tofts_Siemens_Orig'
#directory = 'G:/01-Work/QIICR/DCE BAT project/QIBA/QIBA_v8_Tofts/QIBA_v8_Tofts_GE/S0_5000'
directory = 'G:/01-Work/QIICR/DCE BAT project/QIN Prostate (From TCIA)/QIN PROSTATE'
studyFolders = os.listdir(directory)
fullPathStudyFolders = []
for index,studyFolder in enumerate(studyFolders):
  fullPathStudyFolders.append(os.path.join(directory, studyFolder,'DICOM'))

for i,folder in enumerate(fullPathStudyFolders):
  print studyFolders[i] 

mehrtash

lm = slicer.app.layoutManager()
rw = lm.sliceWidget('Red')
rv = rw.sliceView()
renderWindow = rv.renderWindow()
interactor = renderWindow.GetInteractor()
text = vtk.vtkTextActor()
text.SetInput('PREOP')
textProperty = text.GetTextProperty()
textProperty.SetFontSize(10)
textProperty.SetColor(1,0,0)

mehrtash

import os
dir = os.path.dirname(r'C:\Users\Alz\Desktop\Sync\Repeat_studies (afedorov@partners.org )\7\RESOURCES\801\Segmentations\*')
files = os.listdir(dir)
for fileName in files:
  if "TumorROI_CGTZ_1" in fileName:
    print fileName
    filePath = os.path.join(dir,fileName)
    image = sitk.ReadImage(filePath)
    filter = sitk.BinaryThresholdImageFilter()
    filter.SetLowerThreshold(1)

mehrtash

import numpy as np
ijkToRAS = vtk.vtkMatrix4x4()
labelNode = slicer.util.getNode('label')
labelNode.GetIJKToRASMatrix(ijkToRAS)
centroid = (257.766,264.136,13.6365)
rasPoint = ijkToRAS.MultiplyPoint(centroid + (1,))[:3]
fn = slicer.vtkMRMLMarkupsFiducialNode()
slicer.mrmlScene.AddNode(fn)
fn.AddFiducial(rasPoint[0],rasPoint[1],rasPoint[2])
fn.SetNthFiducialLabel(0,'Centroid')