/
statistic_test.py
137 lines (109 loc) · 5.3 KB
/
statistic_test.py
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# ========================================
# Perform alignment based on Prior Library
# ========================================
import os
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from PIL import Image
from scipy.ndimage import center_of_mass
from medpy.metric import dc
from torchvision import transforms as T
from op.data_op import load_list
from dipy.align import imaffine
from dipy.align import transforms
TEXT_PATH = "Text"
VIEW = ['left']
VISUAL = 'Visual'
PRIOR_DATA = 'PRIOR'
def double_align(tissues_mask, segmentations_mask, rln_and_tissues_mask):
identity = np.eye(3)
c_of_mass = imaffine.transform_centers_of_mass(segmentations_mask, identity, tissues_mask, identity)
n_bins = 32
sampling_prop = None
metric = imaffine.MutualInformationMetric(n_bins, sampling_prop)
level_iter = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affine_reg = imaffine.AffineRegistration(metric=metric, level_iters=level_iter, sigmas=sigmas, factors=factors,
verbosity=0)
transform = transforms.TranslationTransform2D()
params0 = None
starting_affine = c_of_mass.affine
translation = affine_reg.optimize(segmentations_mask, tissues_mask, transform, params0, identity, identity,
starting_affine=starting_affine)
# transformed_img = translation.transform(img, interpolation='linear')
transformed_tissues_mask = translation.transform(tissues_mask, interpolation='nearest')
transformed_rln_and_tissues_mask = translation.transform(rln_and_tissues_mask, interpolation='nearest')
transformed_tissues_mask = transformed_tissues_mask / 50
transformed_tissues_mask = transformed_tissues_mask.astype(np.int32)
transformed_tissues_mask *= 50
transformed_rln_and_tissues_mask = transformed_rln_and_tissues_mask / 50
transformed_rln_and_tissues_mask = transformed_rln_and_tissues_mask.astype(np.int32)
transformed_rln_and_tissues_mask *= 50
return transformed_tissues_mask, transformed_rln_and_tissues_mask
if __name__ == '__main__':
train_list, val_list, test_list = load_list(TEXT_PATH, VIEW)
target_list = test_list
source_list = val_list + train_list
os.makedirs(VISUAL, exist_ok=True)
os.makedirs(PRIOR_DATA, exist_ok=True)
mask_transform = T.Compose([
T.Resize((256, 256), Image.NEAREST),
])
target_dice_list = []
for target_path in target_list[:10]:
patient_id, view_type, item_id = target_path.split('\\')[1:]
segmentations_path = 'Results/{}-{}-{}.png'.format(patient_id, view_type, item_id)
seg_msk = Image.open(segmentations_path).convert('L')
seg_msk_arr = np.array(seg_msk, dtype=np.int32)
target_item_dice_list = []
prior_item_center_list = []
for source_path in tqdm(source_list):
temp_list = []
for idx, mask_item in enumerate(["CCA", "thyroid", "trachea", "RLN"]):
msk = Image.open(os.path.join(source_path, "MASK", "{}.jpg".format(mask_item)))
msk = mask_transform(msk)
msk = np.array(msk) / 255 * (idx + 1)
temp_list.append(msk)
tissues_msk_arr = np.stack(temp_list[:-1], axis=0)
tissues_msk_arr = np.max(tissues_msk_arr, axis=0) * 50
tissues_msk_arr = tissues_msk_arr.astype(np.int32)
rln_msk_arr = np.stack(temp_list, axis=0)
rln_msk_arr = np.max(rln_msk_arr, axis=0) * 50
rln_msk_arr = rln_msk_arr.astype(np.int32)
aligned_tissues_msk, aligned_rln_msk = double_align(tissues_msk_arr, seg_msk_arr, rln_msk_arr)
dice_aligned_seg = dc(aligned_tissues_msk, seg_msk_arr)
target_item_dice_list.append(dice_aligned_seg)
prior_rln_msk = aligned_rln_msk == 200
prior_rln_msk = prior_rln_msk.astype(np.int32)
center_coord = center_of_mass(prior_rln_msk)
prior_item_center_list.append([center_coord[0], center_coord[1], dice_aligned_seg])
target_item_dice_list.sort(reverse=True)
target_dice_list.append(target_item_dice_list)
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.imshow(seg_msk_arr)
for temp in prior_item_center_list:
if temp[2] > 0.85:
s = 15
elif 0.75 > temp[2] > 0.85:
s = 5
else:
s = 1
plt.scatter(x=temp[1], y=temp[0], s=s, alpha=0.3, c="red")
plt.subplot(1, 2, 2)
plt.plot(target_item_dice_list)
plt.title('Sorted Dice score of each aligned prior mask to the segmentation')
plt.xlabel('Subj Id')
plt.ylabel('Dice')
# plt.show()
plt.savefig(os.path.join(VISUAL, '{}-{}-{}.png'.format(patient_id, view_type, item_id)))
plt.close()
prior_item_center_arr = np.array(prior_item_center_list)
np.save(os.path.join(PRIOR_DATA, '{}-{}-{}.npy'.format(patient_id, view_type, item_id)), prior_item_center_arr)
# x = np.arange(1, len(target_item_dice_list) + 1)
plt.figure()
for temp in target_dice_list:
plt.plot(temp)
plt.show()