def get_img(imgloc, segloc=None):
npimg, _, npseg = preprocess.reorient(imgloc, segloc)
npimg = preprocess.resize_to_nn(npimg).astype(np.float32)
npimg = preprocess.window(npimg, -100, 400)
npimg = preprocess.rescale(npimg, -100, 400)
npseg = preprocess.resize_to_nn(npseg).astype(np.uint8)
assert npimg.shape == npseg.shape
return npimg[..., np.newaxis], npseg[..., np.newaxis]
def get_img(imgloc, segloc=None):
npimg, _, npseg = preprocess.reorient(imgloc, segloc)
npimg = preprocess.resize_to_nn(npimg).astype(np.float32)
npimg = preprocess.window(npimg, hu_lb, hu_ub)
npimg = preprocess.rescale(npimg, hu_lb, hu_ub)
npseg = preprocess.resize_to_nn(npseg).astype(np.uint8)
print(npimg.shape)
assert npimg.shape == npseg.shape
return npimg, npseg
def get_mda_imgs(dbfile='/rsrch1/ip/jacctor/livermask/trainingdata_small.csv',
rootloc='/rsrch1/ip/jacctor/LiTS/LiTS'):
nscans = 0
nvalid = 0
nslices = 0
with open(dbfile, 'r') as csvfile:
myreader = csv.DictReader(csvfile, delimiter='\t')
for row in myreader:
imageloc = '%s/%s' % (rootloc, row['image'])
truthloc = '%s/%s' % (rootloc, row['label'])
print(imageloc, truthloc)
nscans += 1
try:
npimg, header, npseg = preprocess.reorient(imageloc,
segloc=truthloc)
nslices += header['dim'][3]
nvalid += 1
except nib.filebasedimages.ImageFileError:
print("could not read file")
print('\ndone precomputing size: ', nslices, ' slices, from ', nvalid,
' scans out of ', nscans, ' scans.\n')
imgs = np.empty((nslices, npx, npx))
segs = np.empty((nslices, npx, npx))
sidx = 0
with open(dbfile, 'r') as csvfile:
myreader = csv.DictReader(csvfile, delimiter='\t')
for row in myreader:
imageloc = '%s/%s' % (rootloc, row['image'])
truthloc = '%s/%s' % (rootloc, row['label'])
print(imageloc, truthloc)
try:
npimg, header, npseg = preprocess.reorient(imageloc,
segloc=truthloc)
npimg = preprocess.resize_to_nn(npimg, transpose=True).astype(
np.int16)
npseg = preprocess.resize_to_nn(npseg, transpose=True).astype(
np.uint8)
sss = header['dim'][3]
imgs[sidx:sidx + sss, ...] = npimg
segs[sidx:sidx + sss, ...] = npseg
sidx += sss
except nib.filebasedimages.ImageFileError:
print("ignoring the file I could't read earlier")
return imgs, segs
def setup_training_from_file():
datacsv = settings.options.dbfile
# create custom data frame database type
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
logfileoutputdir = settings.options.outdir
os.system ('mkdir -p ' + logfileoutputdir)
os.system ('mkdir -p ' + logfileoutputdir + '/data')
os.system ('mkdir -p ' + logfileoutputdir + '/data/img')
os.system ('mkdir -p ' + logfileoutputdir + '/data/seg')
print("Output to\t", logfileoutputdir)
imgdir = logfileoutputdir+'/data/img'
segdir = logfileoutputdir+'/data/seg'
imglist = []
seglist = []
dataidlist = []
with open(datacsv, 'r') as csvfile:
myreader = csv.DictReader(csvfile, delimiter=',')
for row in myreader:
dataid = int(row['dataid'])
imagelocation = '%s/%s' % (settings.options.rootlocation,row['image'])
truthlocation = '%s/%s' % (settings.options.rootlocation,row['label'])
print(imagelocation,truthlocation )
numpyimage, orig_header, numpytruth = preprocess.reorient(imagelocation, segloc=truthlocation)
resimage = preprocess.resize_to_nn(numpyimage, transpose=False).astype(settings.IMG_DTYPE)
resimage = preprocess.window(resimage, settings.options.hu_lb, settings.options.hu_ub)
resimage = preprocess.rescale(resimage, settings.options.hu_lb, settings.options.hu_ub)
restruth = preprocess.resize_to_nn(numpytruth, transpose=False).astype(settings.SEG_DTYPE)
imgROI, segROI = isolate_ROI(resimage, restruth)
Xloc = imgdir+'/volume-'+str(dataid)
Yloc = segdir+'/segmentation-'+str(dataid)
Xlist, Ylist = save_img_into_substacks(imgROI, segROI, Xloc, Yloc)
imglist += Xlist
seglist += Ylist
dataidlist += [dataid]*len(Xlist)
savelistsloc = logfileoutputdir+'/data/datalocations.txt'
with open(savelistsloc, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(['dataid','imgloc','segloc'])
for row in zip(dataidlist, imglist, seglist):
writer.writerow(row)
return savelistsloc
def get_img(imgloc, segloc=None):
npimg, _, npseg = preprocess.reorient(imgloc, segloc)
npimg = preprocess.resize_to_nn(npimg).astype(np.float32)
npimg = preprocess.window(npimg, -100, 200)
npimg = preprocess.rescale(npimg, -100, 200)
npseg = preprocess.resize_to_nn(npseg).astype(np.uint8)
print(npimg.shape)
print(npseg.shape)
assert npimg.shape == npseg.shape
npimg = np.transpose(npimg, (2, 0, 1))
npseg = np.transpose(npseg, (2, 0, 1))
return npimg[..., np.newaxis], npseg[..., np.newaxis]
def get_img(imgloc):
npimg, _, _ = preprocess.reorient(imgloc)
npimg = preprocess.resize_to_nn(npimg, transpose=True).astype(np.int16)
npimg = preprocess.window(npimg, -100,300)
npimg = preprocess.rescale(npimg, -100,300)
print(npimg.shape)
midslice = npimg[int(npimg.shape[0] / 2),:,:]
return midslice[np.newaxis,:,:,np.newaxis]
def PredictModel(model=settings.options.predictmodel,
image=settings.options.predictimage,
imageheader=None,
outdir=settings.options.segmentation):
if (model != None and image != None and outdir != None):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
numpypredict, origheader, _ = preprocess.reorient(image)
assert numpypredict.shape[0:2] == (settings._globalexpectedpixel,
settings._globalexpectedpixel)
resizepredict = preprocess.resize_to_nn(numpypredict)
resizepredict = preprocess.window(resizepredict,
settings.options.hu_lb,
settings.options.hu_ub)
resizepredict = preprocess.rescale(resizepredict,
settings.options.hu_lb,
settings.options.hu_ub)
opt = GetOptimizer()
lss, met = GetLoss()
loaded_model = get_unet()
loaded_model.compile(loss=lss, metrics=met, optimizer=opt)
loaded_model.load_weights(model)
segout_float = loaded_model.predict(resizepredict[...,
np.newaxis])[..., 0]
segout_int = (segout_float >= settings.options.segthreshold).astype(
settings.SEG_DTYPE)
segin_windowed = preprocess.resize_to_original(resizepredict)
segin_windowed_img = nib.Nifti1Image(segin_windowed,
None,
header=origheader)
segin_windowed_img.to_filename(
outdir.replace('.nii', '-imgin-windowed.nii'))
segout_float_resize = preprocess.resize_to_original(segout_float)
segout_float_img = nib.Nifti1Image(segout_float_resize,
None,
header=origheader)
segout_float_img.to_filename(outdir.replace('.nii', '-pred-float.nii'))
segout_int_resize = preprocess.resize_to_original(segout_int)
segout_int_img = nib.Nifti1Image(segout_int_resize,
None,
header=origheader)
segout_int_img.to_filename(outdir.replace('.nii', '-pred-seg.nii'))
return segout_float_resize, segout_int_resize
def PredictNifti(model, saveloc, imageloc, segloc=None):
print('loading data: ', imageloc)
image, origheader, trueseg = preprocess.reorient(imageloc, segloc=segloc)
image = preprocess.resize_to_nn(image, transpose=False).astype(
settings.FLOAT_DTYPE)
image = preprocess.window(image, settings.options.hu_lb,
settings.options.hu_ub)
image = preprocess.rescale(image, settings.options.hu_lb,
settings.options.hu_ub)
image_img = nib.Nifti1Image(image, None, header=origheader)
image_img.to_filename(saveloc + '-img.nii')
predseg_float, predseg = PredictNpy(model, image)
if segloc:
trueseg = preprocess.resize_to_nn(trueseg, transpose=False).astype(
settings.SEG_DTYPE)
names = ['DSC']
metrics = [dsc_l2_3D_npy]
if settings.options.liver:
tseg = (trueseg >= 1).astype(np.int32)[..., np.newaxis]
elif settings.options.tumor:
tseg = (trueseg > 1).astype(np.int32)[..., np.newaxis]
print('pred ', predseg.shape, predseg.dtype, '\ttrue ', tseg.shape,
tseg.dtype)
scores = [
met(tseg.astype(np.int32), predseg.astype(np.float32))
for met in metrics
]
print('DSC:\t', end='')
for idx, s in enumerate(scores):
print(names[idx], '\t', 1.0 - s, end='\t')
print()
print('saving data: ', saveloc)
print(predseg_float.shape)
for i in range(predseg_float.shape[-1]):
segout_float_img = nib.Nifti1Image(predseg_float[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-float-' + str(i) + '.nii')
trueseg_i = (trueseg == i).astype(settings.SEG_DTYPE)
segout_float_img = nib.Nifti1Image(predseg_float[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-truth-' + str(i) + '.nii')
trueseg_img = nib.Nifti1Image(trueseg, None, header=origheader)
trueseg_img.to_filename(saveloc + '-truth.nii')
predseg_img = nib.Nifti1Image(predseg, None, header=origheader)
predseg_img.to_filename(saveloc + '-int.nii')
if settings.options.ttdo and segloc:
print('starting TTDO...')
loaded_model = model.layers[-2]
imagedata = np.transpose(image, (2, 0, 1))[..., np.newaxis]
f = K.function([loaded_model.layers[0].input,
K.learning_phase()], [loaded_model.layers[-1].output])
print('\tgenerating trials...')
results = np.zeros(trueseg.shape + (
1,
settings.options.ntrials,
))
nvalidslices = imagedata.shape[0]
stride = 128
for jj in range(settings.options.ntrials):
zidx = 0
while zidx < nvalidslices:
maxz = min(zidx + stride, nvalidslices)
# out = f([imagedata[zidx:maxz,...], 1])[0]
# out = np.transpose(out, (1,2,0,3))
# results[...,zidx:maxz,0,jj] = out[...,0]
results[..., zidx:maxz, 0, jj] = np.transpose(
f([imagedata[zidx:maxz, ...], 1])[0], (1, 2, 0, 3))[..., 0]
zidx += stride
print('\tcalculating statistics...')
pred_avg = results.mean(axis=-1)
pred_var = results.var(axis=-1)
pred_ent = np.zeros(pred_avg.shape)
ent_idx0 = pred_avg > 0
ent_idx1 = pred_avg < 1
ent_idx = np.logical_and(ent_idx0, ent_idx1)
pred_ent[ent_idx] = -1*np.multiply( pred_avg[ent_idx], np.log( pred_avg[ent_idx])) \
-1*np.multiply(1.0 - pred_avg[ent_idx], np.log(1.0 - pred_avg[ent_idx]))
print('\tsaving statistics...')
# save pred_avg
for i in range(pred_avg.shape[-1]):
segout_float_img = nib.Nifti1Image(pred_avg[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-avg-float-' + str(i) +
'.nii')
pred_avg_int = np.argmax(pred_avg, axis=-1)
predseg_img = nib.Nifti1Image(pred_avg_int, None, header=origheader)
predseg_img.to_filename(saveloc + '-avg-int.nii')
# save pred_var
for i in range(pred_var.shape[-1]):
segout_float_img = nib.Nifti1Image(pred_var[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-var-float-' + str(i) +
'.nii')
# save pred_ent
for i in range(pred_ent.shape[-1]):
segout_float_img = nib.Nifti1Image(pred_ent[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-ent-float-' + str(i) +
'.nii')
return predseg, names, scores
def PredictModel(model=settings.options.predictmodel,
image=settings.options.predictimage,
imageheader=None,
outdir=settings.options.segmentation,
seg=None):
if (model != None and image != None and outdir != None):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
numpypredict, origheader, origseg = preprocess.reorient(image,
segloc=seg)
assert numpypredict.shape[0:2] == (settings._globalexpectedpixel,
settings._globalexpectedpixel)
resizepredict = preprocess.resize_to_nn(numpypredict)
resizepredict = preprocess.window(resizepredict,
settings.options.hu_lb,
settings.options.hu_ub)
resizepredict = preprocess.rescale(resizepredict,
settings.options.hu_lb,
settings.options.hu_ub)
if settings.options.D3 or settings.options.D25:
dataid = np.ones((resizepredict.shape[0]))
idx = np.array([1])
resizepredict2 = thick_slices(resizepredict,
settings.options.thickness, dataid,
idx)
else:
resizepredict2 = resizepredict
if seg:
origseg = preprocess.resize_to_nn(origseg)
origseg = preprocess.livermask(origseg)
if settings.options.D25:
dataid_origseg = np.ones((origseg.shape[0]))
origseg = thick_slices(origseg, 1, dataid_origseg, idx)
origseg = origseg[..., 0]
# if not settings.options.D25 and not settings.options.D3:
# origseg = origseg.transpose((0,2,1)).astype(settings.FLOAT_DTYPE)
origseg_img = nib.Nifti1Image(
preprocess.resize_to_original(origseg), None)
origseg_img.to_filename(outdir.replace('.nii', '-trueseg.nii'))
###
### set up model
###
loaded_model = load_model(model,
custom_objects={
'dsc_l2': dsc_l2,
'l1': l1,
'dsc': dsc,
'dsc_int': dsc,
'ISTA': ISTA
},
compile=False)
#loaded_model.summary()
if settings.options.D25:
segout_float = loaded_model.predict(resizepredict2)[..., 0]
else:
segout_float = loaded_model.predict(
resizepredict2[..., np.newaxis])[..., 0]
segout_int = (segout_float >= settings.options.segthreshold).astype(
settings.SEG_DTYPE)
if settings.options.D3:
segout_float = unthick_slices(segout_float,
settings.options.thickness, dataid,
idx)
segout_int = unthick_slices(segout_int, settings.options.thickness)
elif settings.options.D25:
resizepredict = resizepredict.transpose((0, 2, 1))
#segout_int = preprocess.largest_connected_component(segout_int).astype(settings.SEG_DTYPE)
segin_windowed = preprocess.resize_to_original(resizepredict)
segin_windowed_img = nib.Nifti1Image(segin_windowed,
None,
header=origheader)
segin_windowed_img.to_filename(
outdir.replace('.nii', '-imgin-windowed.nii'))
segout_float_resize = preprocess.resize_to_original(segout_float)
segout_float_img = nib.Nifti1Image(segout_float_resize,
None,
header=origheader)
segout_float_img.to_filename(outdir.replace('.nii', '-pred-float.nii'))
segout_int_resize = preprocess.resize_to_original(segout_int)
segout_int_img = nib.Nifti1Image(segout_int_resize,
None,
header=origheader)
segout_int_img.to_filename(outdir.replace('.nii', '-pred-seg.nii'))
if seg:
#score = dsc_l2_3D(origseg, segout_int)
score = dsc_l2_3D(origseg.astype(settings.FLOAT_DTYPE),
segout_float)
print('dsc:\t', 1.0 - score)
return segout_float_resize, segout_int_resize
def PredictDropout(model=settings.options.predictmodel,
image=settings.options.predictimage,
outdir=settings.options.segmentation,
seg=None):
if not (model != None and image != None and outdir != None):
return
if model is None:
model = settings.options.predictmodel
if outdir is None:
outdir = settings.options.segmentation
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
numpypredict, origheader, origseg = preprocess.reorient(image, segloc=seg)
assert numpypredict.shape[0:2] == (settings._globalexpectedpixel,
settings._globalexpectedpixel)
resizepredict = preprocess.resize_to_nn(numpypredict)
resizepredict = preprocess.window(resizepredict, settings.options.hu_lb,
settings.options.hu_ub)
resizepredict = preprocess.rescale(resizepredict, settings.options.hu_lb,
settings.options.hu_ub)
if seg:
origseg = preprocess.resize_to_nn(origseg)
origseg = preprocess.livermask(origseg)
# save unprocessed image_in
img_in_nii = nib.Nifti1Image(image, None, header=origheader)
img_in_nii.to_filename(outdir.replace('.nii', '-imgin.nii'))
# save preprocessed image_in
segin_windowed_img = nib.Nifti1Image(resizepredict, None)
segin_windowed_img.to_filename(
outdir.replace('.nii', '-imgin-windowed.nii'))
# save true segmentation
if seg:
origseg_img = nib.Nifti1Image(origseg, None)
origseg_img.to_filename(outdir.replace('.nii', '-seg.nii'))
###
### set up model
###
loaded_model = load_model(model,
custom_objects={
'dsc_l2': dsc_l2,
'l1': l1,
'dsc': dsc,
'dsc_int': dsc,
'ISTA': ISTA
})
###
### making baseline prediction and saving to file
###
print('\tmaking baseline predictions...')
segout_float = loaded_model.predict(resizepredict[..., np.newaxis])[..., 0]
segout_int = (segout_float >= settings.options.segthreshold).astype(
settings.SEG_DTYPE)
segout_int = preprocess.largest_connected_component(segout_int).astype(
settings.SEG_DTYPE)
segout_float_img = nib.Nifti1Image(segout_float, None)
segout_float_img.to_filename(outdir.replace('.nii', '-pred-float.nii'))
segout_int_img = nib.Nifti1Image(segout_int, None)
segout_int_img.to_filename(outdir.replace('.nii', '-pred-seg.nii'))
if seg:
score = dsc_l2_3D(origseg, segout_int)
print('\t\t\tdsc:\t', 1.0 - score)
###
### making predictions using different Bernoulli draws for dropout
###
print('\tmaking predictions with different dropouts trials...')
f = K.function([loaded_model.layers[0].input,
K.learning_phase()], [loaded_model.layers[-1].output])
results = np.zeros(resizepredict.shape + (settings.options.ntrials, ))
for jj in range(settings.options.ntrials):
results[..., jj] = f([resizepredict[..., np.newaxis], 1])[0][..., 0]
print('\tcalculating statistics...')
pred_avg = results.mean(axis=-1)
pred_var = results.var(axis=-1)
pred_ent = np.zeros(pred_avg.shape)
ent_idx0 = pred_avg > 0
ent_idx1 = pred_avg < 1
ent_idx = np.logical_and(ent_idx0, ent_idx1)
pred_ent[ent_idx] = -1*np.multiply( pred_avg[ent_idx], np.log( pred_avg[ent_idx])) \
-1*np.multiply(1.0 - pred_avg[ent_idx], np.log(1.0 - pred_avg[ent_idx]))
print('\tsaving trial statistics...')
# save pred_avg
pred_avg_img = nib.Nifti1Image(pred_avg, None)
pred_avg_img.to_filename(outdir.replace('.nii', '-pred-avg.nii'))
# save pred_var
pred_var_img = nib.Nifti1Image(pred_var, None)
pred_var_img.to_filename(outdir.replace('.nii', '-pred-var.nii'))
# save pred_ent
pred_ent_img = nib.Nifti1Image(pred_ent, None)
pred_ent_img.to_filename(outdir.replace('.nii', '-pred-ent.nii'))
print('\n')
return segout_int, segout_float
def PredictNifti(model, saveloc, imageloc, segloc=None):
print('loading data: ', imageloc)
image, origheader, trueseg = preprocess.reorient(imageloc, segloc=segloc)
image = preprocess.resize_to_nn(image, transpose=False).astype(
settings.FLOAT_DTYPE)
image = preprocess.window(image, settings.options.hu_lb,
settings.options.hu_ub)
image = preprocess.rescale(image, settings.options.hu_lb,
settings.options.hu_ub)
image_img = nib.Nifti1Image(image, None, header=origheader)
image_img.to_filename(saveloc + '-img.nii')
predseg_float, predseg = PredictNpy(model, image)
if segloc:
trueseg = preprocess.resize_to_nn(trueseg, transpose=False).astype(
settings.SEG_DTYPE)
# names = ['Background','Liver','Tumor']
# metrics = [dsc_l2_background_npy, dsc_l2_liver_npy, dsc_l2_tumor_npy]
names = ['DSC']
metrics = [dsc_l2_npy]
if settings.options.liver:
tseg = (trueseg >= 1).astype(np.int32)[..., np.newaxis]
elif settings.options.tumor:
tseg = (trueseg > 1).astype(np.int32)[..., np.newaxis]
print('pred ', predseg.shape, predseg.dtype, '\ttrue ', tseg.shape,
tseg.dtype)
scores = [
met(tseg.astype(np.int32), predseg.astype(np.float32))
for met in metrics
]
print('DSC:\t', end='')
for idx, s in enumerate(scores):
print(names[idx], '\t', 1.0 - s, end='\t')
print()
print('saving data: ', saveloc)
print(predseg_float.shape)
for i in range(predseg_float.shape[-1]):
segout_float_img = nib.Nifti1Image(predseg_float[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-float-' + str(i) + '.nii')
trueseg_i = (trueseg == i).astype(settings.SEG_DTYPE)
segout_float_img = nib.Nifti1Image(predseg_float[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-truth-' + str(i) + '.nii')
trueseg_img = nib.Nifti1Image(trueseg, None, header=origheader)
trueseg_img.to_filename(saveloc + '-truth.nii')
predseg_img = nib.Nifti1Image(predseg, None, header=origheader)
predseg_img.to_filename(saveloc + '-int.nii')
if settings.options.ttdo and segloc:
print('starting TTDO...')
f = K.function([loaded_model.layers[0].input,
K.learning_phase()], [loaded_model.layers[-1].output])
print('\tgenerating trials...')
results = np.zeros(trueseg.shape + (
3,
settings.options.ntrials,
))
for jj in range(settings.options.ntrials):
segdata = np.zeros((*image.shape, 3))
nvalidslices = image.shape[2] - settings.options.thickness + 1
for z in range(nvalidslices):
indata = image[np.newaxis, :, :,
z:z + settings.options.thickness, np.newaxis]
segdata[:, :,
z:z + settings.options.thickness, :] += f([indata,
1])[0, ...]
for i in range(settings.options.thickness):
segdata[:, :, i, :] *= (settings.options.thickness) / (i + 1)
for i in range(settings.options.thickness):
segdata[:, :,
-1 - i, :] *= (settings.options.thickness) / (i + 1)
results[..., jj] = segdata / settings.options.thickness
print('\tcalculating statistics...')
pred_avg = results.mean(axis=-1)
pred_var = results.var(axis=-1)
pred_ent = np.zeros(pred_avg.shape)
ent_idx0 = pred_avg > 0
ent_idx1 = pred_avg < 1
ent_idx = np.logical_and(ent_idx0, ent_idx1)
pred_ent[ent_idx] = -1*np.multiply( pred_avg[ent_idx], np.log( pred_avg[ent_idx])) \
-1*np.multiply(1.0 - pred_avg[ent_idx], np.log(1.0 - pred_avg[ent_idx]))
print('\tsaving statistics...')
# save pred_avg
for i in range(pred_avg.shape[-1]):
segout_float_img = nib.Nifti1Image(pred_avg[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-avg-float-' + str(i) +
'.nii')
pred_avg_int = np.argmax(pred_avg, axis=-1)
predseg_img = nib.Nifti1Image(pred_avg_int, None, header=origheader)
predseg_img.to_filename(saveloc + '-avg-int.nii')
# save pred_var
for i in range(pred_var.shape[-1]):
segout_float_img = nib.Nifti1Image(pred_var[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-var-float-' + str(i) +
'.nii')
# save pred_ent
for i in range(pred_ent.shape[-1]):
segout_float_img = nib.Nifti1Image(pred_ent[..., i],
None,
header=origheader)
segout_float_img.to_filename(saveloc + '-ent-float-' + str(i) +
'.nii')
return predseg, names, scores
imgloc = 'C:/Users/sofia/OneDrive/Documents/GitHub/unlinked_livermask/data/LiTS/TrainingBatch2/volume-130.nii'
segloc = 'C:/Users/sofia/OneDrive/Documents/GitHub/unlinked_livermask/data/LiTS/TrainingBatch2/segmentation-130.nii'
#img, seg = get_img(imgloc, segloc)
modelloclist = ['C:/Users/sofia/OneDrive/Documents/GitHub/convswap_shallow.h5']
outloclist = [
'C:/Users/sofia/OneDrive/Documents/GitHub/convswap_images/D2_shallow_test_imgs2/'
]
# preprocess image for prediction
img, origheader, origseg = preprocess.reorient(imgloc, segloc=segloc)
assert img.shape[0:2] == (settings._globalexpectedpixel,
settings._globalexpectedpixel)
img = preprocess.resize_to_nn(img)
img = preprocess.window(img, settings.options.hu_lb, settings.options.hu_ub)
img = preprocess.rescale(img, settings.options.hu_lb, settings.options.hu_ub)
img = img[..., np.newaxis]
# run functions
for j in range(len(outloclist)):
modelloc = modelloclist[j]
outloc = outloclist[j]
vzm, names, mdict = make_viz_model(modelloc)
predict_viz_model(vzm, img, names, mdict, outloc)
## histogram of image pixel values
#def plot_histogram(data, b=100, r=(-110,410)):
# counts, bin_edges = np.histogram(data, bins=b, range=r)
# plt.bar(bin_edges[:-1], counts, width=[0.8*(bin_edges[i+1]-bin_edges[i]) for i in range(len(bin_edges)-1)])
def BuildDB():
# create custom data frame database type
mydatabasetype = [
('dataid', int), ('axialliverbounds', bool),
('axialtumorbounds', bool), ('imagepath', 'S128'),
('imagedata', '(%d,%d)int16' % (settings.options.trainingresample,
settings.options.trainingresample)),
('truthpath', 'S128'),
('truthdata', '(%d,%d)uint8' % (settings.options.trainingresample,
settings.options.trainingresample))
]
# initialize empty dataframe
numpydatabase = np.empty(0, dtype=mydatabasetype)
# load all data from csv
totalnslice = 0
with open(settings.options.dbfile, 'r') as csvfile:
myreader = csv.DictReader(csvfile, delimiter=',')
for row in myreader:
imagelocation = '%s/%s' % (settings.options.rootlocation,
row['image'])
truthlocation = '%s/%s' % (settings.options.rootlocation,
row['label'])
print(imagelocation, truthlocation)
numpyimage, orig_header, numpytruth = preprocess.reorient(
imagelocation, segloc=truthlocation)
# # load nifti file
# imagedata = nib.load(imagelocation)
# numpyimage= imagedata.get_data().astype(settings.IMG_DTYPE )
# error check
assert numpyimage.shape[0:2] == (settings._globalexpectedpixel,
settings._globalexpectedpixel)
nslice = numpyimage.shape[2]
assert numpytruth.shape[0:2] == (settings._globalexpectedpixel,
settings._globalexpectedpixel)
assert nslice == numpytruth.shape[2]
resimage = preprocess.resize_to_nn(
numpyimage, transpose=False).astype(settings.IMG_DTYPE)
restruth = preprocess.resize_to_nn(
numpytruth, transpose=False).astype(settings.SEG_DTYPE)
# bounding box for each label
if (np.max(restruth) == 1):
(liverboundingbox, ) = ndimage.find_objects(restruth)
tumorboundingbox = None
else:
(liverboundingbox,
tumorboundingbox) = ndimage.find_objects(restruth)
if (nslice == restruth.shape[2]):
# custom data type to subset
datamatrix = np.zeros(nslice, dtype=mydatabasetype)
# custom data type to subset
datamatrix['dataid'] = np.repeat(row['dataid'], nslice)
# id the slices within the bounding box
axialliverbounds = np.repeat(False, nslice)
axialtumorbounds = np.repeat(False, nslice)
axialliverbounds[liverboundingbox[2]] = True
if (tumorboundingbox != None):
axialtumorbounds[tumorboundingbox[2]] = True
datamatrix['axialliverbounds'] = axialliverbounds
datamatrix['axialtumorbounds'] = axialtumorbounds
datamatrix['imagepath'] = np.repeat(imagelocation, nslice)
datamatrix['truthpath'] = np.repeat(truthlocation, nslice)
datamatrix['imagedata'] = resimage.transpose(2, 1, 0)
datamatrix['truthdata'] = restruth.transpose(2, 1, 0)
numpydatabase = np.hstack((numpydatabase, datamatrix))
# count total slice for QA
totalnslice = totalnslice + nslice
else:
print('training data error image[2] = %d , truth[2] = %d ' %
(nslice, restruth.shape[2]))
# save numpy array to disk
np.save(settings._globalnpfile, numpydatabase)