def Run(case_folder, case_name):
input_list = []
t2, dwi, adc, prostate, pca = LoadData(case_folder)
_, _, pca = KeepLargest(pca)
slice = SelectMaxRoiSlice(pca)
# slice_list_pca = GetROISlice(pca)
# slice_list_pro = GetROISlice(prostate)
# slice_list = [slice for slice in slice_list_pca if slice in slice_list_pro]
# print(slice_list)
# feature_clinical = r'/home/zhangyihong/Documents/ProstateECE/SUH_Dwi1500/FiveClinical.csv'
# feature_clinical = r'/home/zhangyihong/Documents/ProstateECE/NPYNoDivide/FiveClinicalbGS.csv'
# df = pd.read_csv(feature_clinical, index_col='case')
# info = df.loc[case_name]
# feature_list = [info['age'], info['psa'], info['bGs'], info['core'], info['b-NI']]
# for slice in slice_list:
data_slice_list = []
# center = GetRoiCenterBefore(pca[slice, ...])
center = GetRoiCenterBefore(pca[..., slice])
data_slice_list.append(Process(t2, slice, center))
data_slice_list.append(Process(adc, slice, center))
data_slice_list.append(Process(dwi, slice, center))
data_slice_list.append(Process(prostate, slice, center, is_roi=True))
data_slice_list.append(Process(pca, slice, center, is_roi=True))
# data_slice_list.append(np.array(feature_list))
input_list.append(data_slice_list)
return data_slice_list
def demo():
pred_list, label_list = [], []
for case in sorted(os.listdir(adc_folder)):
# print(case)
# get max pred slice
case_path = os.path.join(H5_path, case[:case.index('_slice')] + '.h5')
slice_preds, label = LoadH5(case_path,
tag=['prediction', 'label'],
data_type=[np.float32, np.uint8])
slice_preds = [1 - pred for pred in slice_preds]
index = slice_preds.index(max(slice_preds))
# get data and slice
t2, dwi, adc, prostate, pca = LoadData(
os.path.join(data_root, case[:case.index('_slice')]))
_, _, pca = KeepLargest(pca)
slice_list_pca = GetROISlice(pca)
slice_list_pro = GetROISlice(prostate)
slice_list = [
slice for slice in slice_list_pca if slice in slice_list_pro
]
# for slice in slice_list:
# center, _ = GetRoiCenter(pca[slice, ...])
# t2_slice_3d = Process(t2, slice, center)
# adc_slice_3d = Process(adc, slice, center)
# dwi_slice_3d = Process(dwi, slice, center)
# pro_slice_3d = Process(prostate, slice, center, is_roi=True)
# pca_slice_3d = Process(pca, slice, center, is_roi=True)
# # data_slice_list.append(np.array(feature_list))
# get npy slice
npy_slice = int(case[case.index('_slice') + 6:case.index('.npy')])
t2_slice = np.load(os.path.join(t2_folder, case))
adc_slice = np.load(os.path.join(adc_folder, case))
dwi_slice = np.load(os.path.join(dwi_folder, case))
pro_slice = np.load(os.path.join(pro_folder, case))
pca_slice = np.load(os.path.join(pca_folder, case))
center = GetRoiCenterBefore(pca[npy_slice, ...])
t2_slice_3d = Process(t2, npy_slice, center)
adc_slice_3d = Process(adc, npy_slice, center)
dwi_slice_3d = Process(dwi, npy_slice, center)
pro_slice_3d = Process(prostate, npy_slice, center, is_roi=True)
pca_slice_3d = Process(pca, npy_slice, center, is_roi=True)
plt.subplot(131)
plt.imshow(np.squeeze(t2[npy_slice, ...]), cmap='gray')
plt.contour(np.squeeze(pca[npy_slice, ...]), colors='r')
plt.scatter(center[1], center[0], marker='o')
plt.subplot(132)
plt.imshow(np.squeeze(t2_slice), cmap='gray')
plt.scatter(140, 140, marker='o')
plt.subplot(133)
plt.imshow(np.squeeze(t2_slice_3d), cmap='gray')
plt.scatter(140, 140, marker='o')
plt.show()
def CheckROINum(data_folder):
roi_folder = os.path.join(data_folder, 'cancer_slice')
ct_folder = os.path.join(data_folder, 'ct_slice')
gland_folder = os.path.join(data_folder, 'kindey_slice')
for case in os.listdir(roi_folder):
roi = np.squeeze(np.load(os.path.join(roi_folder, case)))
# gland = np.squeeze(np.load(os.path.join(gland_folder, case)))
# ct = np.squeeze(np.load(os.path.join(ct_folder, case)))
_, num, new_roi = KeepLargest(roi)
if num > 1:
print(case)
def WriteH5(data_folder, save_path):
case_list = os.listdir(data_folder)
crop_shape = (1, 280, 280)
for case in case_list:
# path
case_path = os.path.join(data_folder, case)
t2_path = os.path.join(case_path, 't2.nii')
roi_path = os.path.join(case_path, 'roi.nii')
dwi_path = os.path.join(case_path, 'dwi_Reg.nii')
adc_path = os.path.join(case_path, 'adc_Reg.nii')
ece = info.loc[case, 'pECE']
if ece == 0:
ece = np.array([0, 1], dtype=np.uint8)
elif ece == 1:
ece = np.array([1, 0], dtype=np.uint8)
# Load data
_, t2, _ = LoadNiiData(t2_path, dtype=np.float32)
_, dwi, _ = LoadNiiData(dwi_path, dtype=np.float32)
_, adc, _ = LoadNiiData(adc_path, dtype=np.float32)
_, roi, _ = LoadNiiData(roi_path, dtype=np.uint8)
_, _, new_roi = KeepLargest(roi)
slice = SelectMaxRoiSlice(new_roi)
center = GetRoiCenter(new_roi[slice, ...])
t2_slice = CropT2Data(t2, crop_shape, slice, center=center)
dwi_slice = CropT2Data(dwi, crop_shape, slice, center=center)
adc_slice = CropT2Data(adc, crop_shape, slice, center=center)
roi_slice = CropRoiData(new_roi, crop_shape, slice, center=center)
t2_slice_3d = t2_slice[np.newaxis, ...]
dwi_slice_3d = dwi_slice[np.newaxis, ...]
adc_slice_3d = adc_slice[np.newaxis, ...]
roi_slice_3d = roi_slice[np.newaxis, ...]
dataname = case + '_slice' + str(slice) + '.h5'
datapath = os.path.join(save_path, dataname)
with h5py.File(datapath, 'w') as f:
f['input_0'] = t2_slice_3d
f['input_1'] = dwi_slice_3d
f['input_2'] = adc_slice_3d
f['output_0'] = roi_slice_3d
f['output_1'] = ece
def CropKeepLargest():
data_folder = r'/home/zhangyihong/Documents/Kindey901/Kindey_npy'
roi_folder = os.path.join(data_folder, 'cancer_slice')
gland_folder = os.path.join(data_folder, 'kindey_slice')
ct_folder = os.path.join(data_folder, 'ct_slice')
shape = (300, 300)
for case in os.listdir(roi_folder):
case = '601548_-_slice8.npy'
ct = np.squeeze(np.load(os.path.join(ct_folder, case)))
roi = np.squeeze(np.load(os.path.join(roi_folder, case)))
gland = np.squeeze(np.load(os.path.join(gland_folder, case)))
center, _ = GetRoiCenter(roi)
roi_new, _ = ExtractPatch(roi, patch_size=shape, center_point=center)
gland_new, _ = ExtractPatch(gland,
patch_size=shape,
center_point=center)
ct_new, _ = ExtractPatch(ct, patch_size=shape, center_point=center)
_, _, gland_new = KeepLargest(gland_new)
ct_slice_3d = ct_new[np.newaxis, ...]
gland_slice_3d = gland_new[np.newaxis, ...]
roi_slice_3d = roi_new[np.newaxis, ...]
plt.subplot(121)
plt.imshow(ct, cmap='gray')
plt.contour(gland, colors='r')
plt.contour(roi, colors='y')
plt.scatter(x=center[1], y=center[0])
plt.subplot(122)
plt.imshow(ct_new, cmap='gray')
plt.contour(gland_new, colors='r')
plt.contour(roi_new, colors='y')
plt.scatter(x=150, y=150)
plt.show()
np.save(os.path.join(ct_folder, case), ct_slice_3d)
np.save(os.path.join(roi_folder, case), roi_slice_3d)
np.save(os.path.join(gland_folder, case), gland_slice_3d)
break
# slice_pred = r'/home/zhangyihong/Documents/ProstateECE/Result/PAGNet_test.csv'
slice_df = pd.read_csv(csv_path, index_col='case')
pred_2d = slice_df.loc[case]['Pred']
label_2d = slice_df.loc[case]['Label']
return pred_2d, label_2d
def GetSliceList(case, folder, is_show=False):
t2, _, _, prostate, pca = LoadData(os.path.join(folder, case))
if is_show:
Imshow3DArray(Normalize01(t2.transpose(1, 2, 0)),
roi=[
Normalize01(prostate.transpose(1, 2, 0)),
Normalize01(pca.transpose(1, 2, 0))
])
_, _, pca = KeepLargest(pca)
slice_list_pca = GetROISlice(pca)
slice_list_pro = GetROISlice(prostate)
slice_list = [slice for slice in slice_list_pca if slice in slice_list_pro]
return slice_list
def Compare(case):
# get 2d
slice_pred = r'/home/zhangyihong/Documents/ProstateECE/Result/PAGNet_suh.csv'
pred_2d, label_2d = Get2DPred(case, slice_pred)
# get 3d
preds, label_3d = Get3DPred(case, H5_path, if_negative=True)
slice_list = GetSliceList(case, data_root)
distance_save_path = os.path.join(save_folder, 'DistanceMap')
# Load data
_, t2, _ = LoadImage(t2_path, dtype=np.float32)
_, dwi, _ = LoadImage(dwi_path, dtype=np.float32)
_, adc, _ = LoadImage(adc_path, dtype=np.float32)
_, pca, _ = LoadImage(roi_path, dtype=np.uint8)
_, prostate, _ = LoadImage(prostate_path, dtype=np.uint8)
t2 = t2.transpose((2, 0, 1))
dwi = dwi.transpose((2, 0, 1))
adc = adc.transpose((2, 0, 1))
pca = pca.transpose((2, 0, 1))
prostate = prostate.transpose((2, 0, 1))
_, _, new_roi = KeepLargest(pca)
slice_list_pca = GetROISlice(pca)
slice_list_pro = GetROISlice(prostate)
slice_list = [slice for slice in slice_list_pca if slice in slice_list_pro]
slice_preds, label = Get3DPred(case, pred_path, if_negative=True)
slice = slice_list[slice_preds.index(max(slice_preds))]
center = GetRoiCenterBefore(new_roi[slice, ...])
t2_slice = CropT2Data(t2, crop_shape, slice, center=center)
dwi_slice = CropT2Data(dwi, crop_shape, slice, center=center)
adc_slice = CropT2Data(adc, crop_shape, slice, center=center)
roi_slice = CropRoiData(new_roi, crop_shape, slice, center=center)
prostate_slice = CropRoiData(prostate, crop_shape, slice, center=center)
distance_map = FindRegion(prostate_slice, roi_slice)