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test_3d_patch.py
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146 lines (124 loc) · 5.64 KB
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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
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_LA(model, num_classes, patch_size=(112, 112, 80), stride_xy=18, stride_z=4):
with open('../data/LA/test.list', 'r') as f:
image_list = f.readlines()
image_list = ["../data/LA/2018LA_Seg_Training Set/" + item.replace('\n', '') + "/mri_norm2.h5" for item in image_list]
loader = tqdm(image_list)
total_dice = 0.0
for image_path in loader:
h5f = h5py.File(image_path, 'r')
image = h5f['image'][:]
label = h5f['label'][:]
prediction, score_map = test_single_case(model, image, stride_xy, stride_z, patch_size, num_classes=num_classes)
if np.sum(prediction)==0:
dice = 0
else:
dice = metric.binary.dc(prediction, 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, 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=0, nms=0):
loader = tqdm(image_list) if not metric_detail else image_list
total_metric = 0.0
ith = 0
for image_path in loader:
# id = image_path.split('/')[-2]
h5f = h5py.File(image_path, 'r')
image = h5f['image'][:]
label = h5f['label'][:]
if preproc_fn is not None:
image = preproc_fn(image)
prediction, score_map = test_single_case(model, image, stride_xy, stride_z, patch_size, num_classes=num_classes)
if nms:
prediction = getLargestCC(prediction)
if np.sum(prediction)==0:
single_metric = (0,0,0,0)
else:
single_metric = calculate_metric_percase(prediction, label[:])
if metric_detail:
print('%02d,\t%.5f, %.5f, %.5f, %.5f' % (ith, single_metric[0], single_metric[1], single_metric[2], single_metric[3]))
total_metric += np.asarray(single_metric)
if save_result:
nib.save(nib.Nifti1Image(prediction.astype(np.float32), np.eye(4)), test_save_path + "%02d_pred.nii.gz" % ith)
nib.save(nib.Nifti1Image(score_map[0].astype(np.float32), np.eye(4)), test_save_path + "%02d_scores.nii.gz" % ith)
ith += 1
avg_metric = total_metric / len(image_list)
print('average metric is {}'.format(avg_metric))
with open(test_save_path+'../performance.txt', 'w') as f:
f.writelines('average metric is {} \n'.format(avg_metric))
return avg_metric
def test_single_case(model, image, stride_xy, stride_z, patch_size, num_classes=1):
w, h, d = image.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 = np.pad(image, [(wl_pad,wr_pad),(hl_pad,hr_pad), (dl_pad, dr_pad)], mode='constant', constant_values=0)
ww,hh,dd = image.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 = np.zeros((num_classes, ) + image.shape).astype(np.float32)
cnt = np.zeros(image.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 = image[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]]
test_patch = np.expand_dims(np.expand_dims(test_patch,axis=0),axis=0).astype(np.float32)
test_patch = torch.from_numpy(test_patch).cuda()
with torch.no_grad():
y1, features = model(test_patch)
y = F.softmax(y1, dim=1)
y = y.cpu().data.numpy()
y = y[0,1,:,:,:]
score_map[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \
= score_map[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + y
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 = score_map/np.expand_dims(cnt,axis=0)
label_map = (score_map[0]>0.5).astype(np.int)
if add_pad:
label_map = label_map[wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
score_map = score_map[:,wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]
return label_map, score_map
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)
return dice, jc, hd, asd