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test_patch.py
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test_patch.py
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import h5py
import math
import nibabel as nib
import numpy as np
from medpy import metric
import torch
import torch.nn.functional as F
from tqdm import tqdm
from skimage.measure import label
import numpy as np
def getLargestCC(segmentation):
labels = label(segmentation)
assert( labels.max() != 0 ) # assume at least 1 CC
largestCC = labels == np.argmax(np.bincount(labels.flat)[1:])+1
return largestCC
def var_all_case(model, num_classes, patch_size=(112, 112, 80), stride_xy=18, stride_z=4):
with open('./data/val.list', 'r') as f:
image_list = f.readlines()
image_list = [item.replace('\n','') for item in image_list]
loader = tqdm(image_list)
total_dice = 0.0
for image_path in loader:
if "MSSEG2" in image_path:
h5f = h5py.File(image_path, 'r')
image_1 = h5f['image_1'][:]
image_2 = h5f['image_2'][:]
label = h5f['label'][:]
_, _, prediction_sub, _, _, _ = test_single_case_all(model, image_1, image_2, stride_xy, stride_z, patch_size, num_classes=num_classes)
if np.sum(prediction_sub)==0:
dice = 0
else:
dice = metric.binary.dc(prediction_sub, label)
total_dice += dice
else:
h5f = h5py.File(image_path, 'r')
image_1 = h5f['image'][:]
image_2 = h5f['image'][:]
label = h5f['label'][:]
prediction_1, _, _, _, _, _ = test_single_case_all(model, image_1, image_2, stride_xy, stride_z, patch_size, num_classes=num_classes)
if np.sum(prediction_1)==0:
dice = 0
else:
dice = metric.binary.dc(prediction_1, label)
total_dice += dice
avg_dice = total_dice / len(image_list)
print('average metric is {}'.format(avg_dice))
return avg_dice
def test_all_case(model_name, num_outputs, model, image_list, num_classes, patch_size=(112, 112, 80), stride_xy=18, stride_z=4, save_result=True, test_save_path=None, preproc_fn=None, metric_detail=1, nms=0):
loader = tqdm(image_list) if not metric_detail else image_list
ith = 0
total_metric1 = 0.0
total_metric2 = 0.0
for image_path in loader:
if "MSSEG2" in image_path:
h5f = h5py.File(image_path, 'r')
image_1 = h5f['image_1'][:]
image_2 = h5f['image_2'][:]
label = h5f['label'][:]
if preproc_fn is not None:
image = preproc_fn(image)
prediction_1, _, prediction_sub, _, _, _ = test_single_case_all(model, image_1, image_2, stride_xy, stride_z, patch_size, num_classes=num_classes)
_, prediction_2, _, _, _, _ = test_single_case_all(model, image_2, image_2, stride_xy, stride_z, patch_size, num_classes=num_classes)
prediction = prediction_sub
if nms:
prediction = getLargestCC(prediction)
if np.sum(prediction)==0:
single_metric = (0,0,0,0)
else:
single_metric = calculate_metric_percase(prediction, label[:])
total_metric1 += np.asarray(single_metric)
else:
h5f = h5py.File(image_path, 'r')
image_1 = h5f['image'][:]
image_2 = h5f['image'][:]
label = h5f['label'][:]
if preproc_fn is not None:
image = preproc_fn(image)
prediction_1, prediction_2, prediction_sub, score_map_1, score_map_2, score_map_sub = test_single_case_all(model, image_1, image_2, stride_xy, stride_z, patch_size, num_classes=num_classes)
prediction = prediction_1
if nms:
prediction = getLargestCC(prediction)
if np.sum(prediction)==0:
single_metric = (0,0,0,0)
else:
single_metric = calculate_metric_percase(prediction, label[:])
total_metric2 += np.asarray(single_metric)
if save_result:
nib.save(nib.Nifti1Image(prediction_1.astype(np.float32), np.eye(4)), test_save_path + "%02d_pred_1.nii.gz" % ith)
nib.save(nib.Nifti1Image(prediction_2.astype(np.float32), np.eye(4)), test_save_path + "%02d_pred_2.nii.gz" % ith)
nib.save(nib.Nifti1Image(prediction_sub.astype(np.float32), np.eye(4)), test_save_path + "%02d_pred_sub.nii.gz" % ith)
nib.save(nib.Nifti1Image(image_1[:].astype(np.float32), np.eye(4)), test_save_path + "%02d_img_1.nii.gz" % ith)
nib.save(nib.Nifti1Image(image_2[:].astype(np.float32), np.eye(4)), test_save_path + "%02d_img_2.nii.gz" % ith)
nib.save(nib.Nifti1Image((image_2-image_1).astype(np.float32), np.eye(4)), test_save_path + "%02d_img_sub.nii.gz" % ith)
nib.save(nib.Nifti1Image(label[:].astype(np.float32), np.eye(4)), test_save_path + "%02d_gt.nii.gz" % ith)
ith += 1
avg_metric1 = total_metric1 / 8
avg_metric2 = total_metric2 / 8
print('average metric_public_(dice, jc, hd, asd, precision, se, sp, F1): {}'.format(avg_metric1))
print('average metric_inhouse_(dice, jc, hd, asd, precision, se, sp, F1): {}'.format(avg_metric2))
with open(test_save_path+'../{}_performance.txt'.format(model_name), 'w') as f:
f.writelines('average metric_public_(dice, jc, hd, asd, precision, se, sp, F1): {}'.format(avg_metric1))
f.writelines('average metric_inhouse_(dice, jc, hd, asd, precision, se, sp, F1): {}'.format(avg_metric2))
return avg_metric1
def test_single_case_all(model, image_1, image_2, stride_xy, stride_z, patch_size, num_classes=1):
w, h, d = image_1.shape
# if the size of image is less than patch_size, then padding it
add_pad = False
if w < patch_size[0]:
w_pad = patch_size[0]-w
add_pad = True
else:
w_pad = 0
if h < patch_size[1]:
h_pad = patch_size[1]-h
add_pad = True
else:
h_pad = 0
if d < patch_size[2]:
d_pad = patch_size[2]-d
add_pad = True
else:
d_pad = 0
wl_pad, wr_pad = w_pad//2,w_pad-w_pad//2
hl_pad, hr_pad = h_pad//2,h_pad-h_pad//2
dl_pad, dr_pad = d_pad//2,d_pad-d_pad//2
if add_pad:
image_1 = np.pad(image_1, [(wl_pad,wr_pad),(hl_pad,hr_pad), (dl_pad, dr_pad)], mode='constant', constant_values=0)
image_2 = np.pad(image_2, [(wl_pad,wr_pad),(hl_pad,hr_pad), (dl_pad, dr_pad)], mode='constant', constant_values=0)
ww,hh,dd = image_1.shape
sx = math.ceil((ww - patch_size[0]) / stride_xy) + 1
sy = math.ceil((hh - patch_size[1]) / stride_xy) + 1
sz = math.ceil((dd - patch_size[2]) / stride_z) + 1
# print("{}, {}, {}".format(sx, sy, sz))
score_map_1 = np.zeros((num_classes, ) + image_1.shape).astype(np.float32)
score_map_2 = np.zeros((num_classes, ) + image_1.shape).astype(np.float32)
score_map_sub = np.zeros((num_classes, ) + image_1.shape).astype(np.float32)
cnt = np.zeros(image_1.shape).astype(np.float32)
for x in range(0, sx):
xs = min(stride_xy*x, ww-patch_size[0])
for y in range(0, sy):
ys = min(stride_xy * y,hh-patch_size[1])
for z in range(0, sz):
zs = min(stride_z * z, dd-patch_size[2])
test_patch_1 = image_1[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]]
test_patch_1 = np.expand_dims(np.expand_dims(test_patch_1,axis=0),axis=0).astype(np.float32)
test_patch_1 = torch.from_numpy(test_patch_1).cuda()
test_patch_2 = image_2[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]]
test_patch_2 = np.expand_dims(np.expand_dims(test_patch_2,axis=0),axis=0).astype(np.float32)
test_patch_2 = torch.from_numpy(test_patch_2).cuda()
test_sub = test_patch_2-test_patch_1
test_patch = torch.cat([test_patch_1, test_patch_2, test_sub], dim=1)
with torch.no_grad():
y1, y2, y3 = model(test_patch)
y1, y2, y3 = F.softmax(y1, dim=1), F.softmax(y2, dim=1), F.softmax(y3, dim=1)
y1, y2, y3 = y1.cpu().data.numpy(), y2.cpu().data.numpy(), y3.cpu().data.numpy()
y1, y2, y3 = y1[0,1,:,:,:], y2[0,1,:,:,:], y3[0,1,:,:,:]
score_map_1[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \
= score_map_1[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + y1
score_map_2[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \
= score_map_2[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + y2
score_map_sub[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \
= score_map_sub[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + y3
cnt[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \
= cnt[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + 1
score_map_1 = score_map_1/np.expand_dims(cnt,axis=0)
score_map_2 = score_map_2/np.expand_dims(cnt,axis=0)
score_map_sub = score_map_sub/np.expand_dims(cnt,axis=0)
label_map_1 = (score_map_1[0]>0.5).astype(np.int)
label_map_2 = (score_map_2[0]>0.5).astype(np.int)
label_map_sub = (score_map_sub[0]>0.5).astype(np.int)
if add_pad:
label_map_1 = label_map_1[wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
label_map_2 = label_map_2[wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
label_map_sub = label_map_sub[wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
score_map_1 = score_map_1[:,wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
score_map_2 = score_map_2[:,wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
score_map_sub = score_map_sub[:,wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
return label_map_1, label_map_2, label_map_sub, score_map_1, score_map_2, score_map_sub
def calculate_metric_percase(pred, gt):
dice = metric.binary.dc(pred, gt)
jc = metric.binary.jc(pred, gt)
hd = metric.binary.hd95(pred, gt)
asd = metric.binary.asd(pred, gt)
precision = metric.binary.precision(pred, gt)
se = metric.binary.sensitivity(pred, gt)
sp = metric.binary.specificity(pred, gt)
label_gt = label(gt)
label_gts = np.bincount(label_gt.flat)
label_pred = label(pred)
label_preds = np.bincount(label_pred.flat)
M, N = label_gts.shape[0], label_preds.shape[0]
index = np.where(label_gts<11)
idx_offset = 0
if index[0].size !=0:
for idx in range(index[0].shape[0]):
mask = label_gt==index[0][idx]-idx_offset
label_gt[mask]=0
# we need to close the gap after removing the label
label_gt[label_gt>index[0][idx]-idx_offset] -=1
idx_offset += 1
M=M-1
index = np.where(label_preds<11)
idx_offset = 0
if index[0].size !=0:
for idx in range(index[0].shape[0]):
mask = label_pred==index[0][idx]-idx_offset
label_pred[mask]=0
# we need to close the gap after removing the label
label_pred[label_pred>index[0][idx]-idx_offset] -=1
idx_offset += 1
N=N-1
H_ij = np.zeros((M, N))
for i in range(M):
for j in range(N):
H_ij[i, j] = ((label_gt==i) * (label_pred==j)).sum()
TPg=0
for i in range(1, M):
alpha = H_ij[i, 1:].sum() / (H_ij[i, :].sum() + 1e-18)
if alpha > 0.1:
wsum, k, vaccept=0, 0, True
while wsum < 0.65:
pk = np.argsort(-H_ij[i, 1:])[k]+1#np.argwhere(np.argsort(H_ij[i])==k)[0][0]
tk = H_ij[0, pk] / H_ij[:, pk].sum()
if tk >0.7:
vaccept = False
break
wsum += H_ij[i, pk] / H_ij[i, 1:].sum()
k +=1
if vaccept == True:
TPg +=1
TPa=0
H_ji = H_ij.T
for j in range(1, N):
alpha = H_ji[j, 1:].sum() / (H_ji[j, :].sum()+ 1e-18)
if alpha > 0.1:
wsum, k, vaccept=0, 0, True
while wsum < 0.65:
pk = np.argsort(-H_ji[j, 1:])[k]+1#np.argwhere(np.argsort(H_ji[j])==k)[0][0]
tk = H_ji[0, pk] / H_ji[:, pk].sum()
if tk >0.7:
vaccept = False
break
wsum += H_ji[j, pk] / H_ji[j, 1:].sum()
k +=1
if vaccept == True:
TPa +=1
sel, pl = TPg/(M-1),TPa/(N-1)
if sel == 0 or pl == 0:
F1 = 0
else:
F1 = (2 * sel * pl) / (sel+pl)
print("TPg:{}, M:{}, TPa:{}, N:{}".format(TPg, M-1, TPa, N-1))
print("sel:{}, pl:{}, f1:{}".format(sel, pl, F1))
print("dice:{}, jc:{}, 95hd:{}, asd:{}, pr:{}, se:{}, sp:{}".format(dice, jc, hd, asd, precision, se, sp))
return dice, jc, hd, asd, precision, se, sp, F1