File size: 2,593 Bytes
0803a67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | from scipy.ndimage import label
import nibabel as nib
import cc3d
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import os
def voxel2mm(N, spacing):
return int(N * np.prod(spacing))
def size2r(size):
r = np.cbrt(3 * size / (4 * np.pi))
return r
def pred_size_threshold_postprocess2(pred_volume, spacing_mm=[1,1,1]):
pred_les_numeric, pred_les_N = cc3d.connected_components(pred_volume, connectivity=26, return_N=True)
for segid in range(1, pred_les_N+1):
extracted_image = np.uint8(pred_les_numeric * (pred_les_numeric == segid)>0)
pred_size = np.sum(extracted_image)
# if pred_size < size_threshold:
# pred_les_numeric = pred_les_numeric * (1-extracted_image)
# continue
size = voxel2mm(pred_size, spacing_mm)
r = size2r(size)
if r<3:
pred_les_numeric = pred_les_numeric * (1-extracted_image)
pred_volume_ = np.uint8(pred_les_numeric > 0)
return pred_volume_
def pred_size_threshold_postprocess(pred_volume_cls, size_threshold=20):
pred_volume_cls_binary = np.uint8(pred_volume_cls>0)
pred_les_numeric, pred_les_N = cc3d.connected_components(pred_volume_cls_binary, connectivity=26, return_N=True)
for segid in range(1, pred_les_N+1):
extracted_image = np.uint8(pred_les_numeric * (pred_les_numeric == segid)>0)
pred_size = np.sum(extracted_image)
if pred_size < size_threshold:
pred_les_numeric = pred_les_numeric * (1-extracted_image)
pred_volume_cls = np.uint8(pred_les_numeric > 0) * pred_volume_cls
return pred_volume_cls
def load_pred(pred_path):
pred_volume = nib.load(pred_path)
affine = pred_volume.affine
pixdim = pred_volume.header['pixdim']
spacing_mm = tuple(pixdim[1:4])
pred_volume = pred_volume.get_fdata() # HxWxC (Numeric)
return pred_volume, spacing_mm, affine
file_root = 'out/pancreas/pancreas.segresnet/pancreas.segresnet/0.75/pred'
save_root = 'out/pancreas/pancreas.segresnet/pancreas.segresnet/0.75/pred_post2'
os.makedirs(save_root, exist_ok=True)
file_names = os.listdir(file_root)
file_names.sort()
for file in file_names:
print(file)
pred_volume, spacing_mm, affine = load_pred(os.path.join(file_root, file))
# pred_volume = pred_size_threshold_postprocess(pred_volume)
pred_volume = pred_size_threshold_postprocess2(pred_volume, spacing_mm)
nib.save(
nib.Nifti1Image(pred_volume.astype(np.uint8), affine), os.path.join(save_root, file)
)
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