553 lines
16 KiB
Python
553 lines
16 KiB
Python
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import json
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import os
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import shelve
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import shutil
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import time
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import ants
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import matplotlib.pyplot as plt
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import numpy as np
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import SimpleITK as sitk
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PATIENTS_ROOT = '/mnt/1220/Public/dataset2/G4'
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OUT_ROOT = '/home/xfr/git9/Taipei-1/0'
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OUT_ROOT = '/mnt/1220/Public/dataset2/G5'
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SHELVE = os.path.join(OUT_ROOT, '0shelve')
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MAX_Y = 256
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SIZE_X = 249
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SIZE_Y = 249
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SIZE_Z = 192
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# SIZE_Z = 256
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MIN_OVERLAP = 0.50
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MIN_METRIC = -0.50
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# def resize_with_crop_or_pad(image, tx = SIZE_X, ty = SIZE_Y, tz = SIZE_Z):
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def resize_with_pad(image, tx = SIZE_X, ty = SIZE_Y, tz = SIZE_Z):
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sx, sy, sz = image.GetSize()
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l = [(tx-sx)//2,
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(ty-sy)//2,
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(tz-sz)//2,]
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u = [tx-sx-l[0],
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ty-sy-l[1],
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tz-sz-l[2],
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]
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# print (l, u)
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return sitk.ConstantPad(image, l, u)
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def draw_sitk(image, d, post):
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a = sitk.GetArrayFromImage(image)
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s = a.shape
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fig, axs = plt.subplots(1, 3)
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# fig.suptitle('%dx%dx%d'%(s[2], s[1], s[0]))
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axs.flat[0].imshow(a[s[0]//2,:,:], cmap='gray')
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axs.flat[1].imshow(a[:,s[1]//2,:], cmap='gray')
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axs.flat[2].imshow(a[:,:,s[2]//2], cmap='gray')
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axs.flat[0].axis('off')
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axs.flat[1].axis('off')
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axs.flat[2].axis('off')
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axs.flat[1].invert_yaxis()
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axs.flat[2].invert_yaxis()
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plt.tight_layout()
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os.makedirs(d, exist_ok=True)
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plt.savefig(os.path.join(d, '%dx%dx%d-%s'%(s[2],s[1],s[0],post)))
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plt.close()
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# exit()
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def bbox2_3D(img):
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r = np.any(img, axis=(1, 2))
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c = np.any(img, axis=(0, 2))
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z = np.any(img, axis=(0, 1))
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if not np.any(r):
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return -1, -1, -1, -1, -1, -1
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rmin, rmax = np.where(r)[0][[0, -1]]
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cmin, cmax = np.where(c)[0][[0, -1]]
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zmin, zmax = np.where(z)[0][[0, -1]]
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return rmin, rmax, cmin, cmax, zmin, zmax
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def physical_size(a):
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A = a.numpy().shape
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B = a.spacing
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C = (
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A[0]*B[0],
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A[1]*B[1],
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A[2]*B[2],
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)
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print(A,B,C)
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return C
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def registration(ct0, ct1, mr, modality = 'CT'):
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# print(
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# physical_size(ct0),
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# physical_size(moving)
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# )
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TX = []
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FIXS = (ct0, ct1)
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# FIXS = (ct1,)
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TYPES = (
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# 'Translation',
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'Rigid',
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'QuickRigid',
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'DenseRigid',
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'BOLDRigid',
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# 'Similarity',
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# 'Affine',
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# 'AffineFast',
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# 'BOLDAffine',
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# 'TRSAA',
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)
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if modality == 'CT':
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FIXS = (ct1, ct0)
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# TYPES = (
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# # 'Translation',
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# 'Rigid',
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# )
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start = time.time()
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for ff in range(len(FIXS)):
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for typ in TYPES:
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ct = FIXS[ff]
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print(typ)
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# '''
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mytx = ants.registration(ct, mr, typ)
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ones = np.ones(mr.numpy().shape)
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mask1 = mr.new_image_like(ones)
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mytx['mask'] = ants.apply_transforms(ct0, mask1, mytx['fwdtransforms'],
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interpolator='genericLabel',
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)
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am = ants.create_ants_metric(ct,
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mytx['warpedmovout'],
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metric_type='MattesMutualInformation',
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moving_mask = mytx['mask'],
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)
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mytx['metricFM'] = am.get_value()
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am = ants.create_ants_metric(mr,
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mytx['warpedfixout'],
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metric_type='MattesMutualInformation')
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mytx['metricMM'] = am.get_value()
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mytx['metric'] = min([mytx['metricFM'], mytx['metricMM']])
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mytx['ct'] = ff
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mytx['type'] = (typ, 'fwd')
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mytx['warpedout'] = mytx['warpedmovout']
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TX.append(mytx)
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print(mytx['metric'], mytx['metricFM'], mytx['metricMM'])
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# '''
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mytx = ants.registration(mr, ct, typ)
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ones = np.ones(mr.numpy().shape)
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mask1 = mr.new_image_like(ones)
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mytx['mask'] = ants.apply_transforms(ct0, mask1, mytx['fwdtransforms'],
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interpolator='genericLabel',
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whichtoinvert=[True],
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)
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am = ants.create_ants_metric(mr,
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mytx['warpedmovout'],
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metric_type='MattesMutualInformation')
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mytx['metricFM'] = am.get_value()
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am = ants.create_ants_metric(ct,
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mytx['warpedfixout'],
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metric_type='MattesMutualInformation',
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fixed_mask = mytx['mask'],
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)
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mytx['metricMM'] = am.get_value()
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mytx['metric'] = min([mytx['metricFM'], mytx['metricMM']])
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mytx['ct'] = ff
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mytx['type'] = (typ, 'inv')
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mytx['warpedout'] = mytx['warpedfixout']
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TX.append(mytx)
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print(mytx['metric'], mytx['metricFM'], mytx['metricMM'])
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if min([t['metric'] for t in TX]) < MIN_METRIC:
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break
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if min([t['metric'] for t in TX]) < MIN_METRIC:
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break
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print(time.time()-start, 'seconds')
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tx = {
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'metric': 0,
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}
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for t in TX:
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if t['metric'] < tx['metric']:
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tx = t
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tx['ratio'] = tx['mask'].numpy().sum() / np.prod(ct0.numpy().shape)
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return tx
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def check(epath):
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roi=None
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for root, dirs, files in os.walk(epath):
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dirs.sort()
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RT_DIR = os.path.join(root, 'RT')
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EXTERNAL_DIR = os.path.join(RT_DIR, 'EXTERNAL')
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if not os.path.isdir(EXTERNAL_DIR):
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continue
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ORGAN_DIR = os.path.join(RT_DIR, 'ORGAN')
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if not os.path.isdir(ORGAN_DIR):
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continue
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# if there is no eye, it's no a brain image
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eye = None
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organs = sorted(os.scandir(ORGAN_DIR), key=lambda e: e.name)
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for o in organs:
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if 'eye' in o.name.lower():
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eye = o
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if eye is None:
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print('no eye... skip', root)
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# exit()
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return None
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CT_IMAGE = os.path.join(RT_DIR, 'ct_image.nii.gz')
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externals = sorted(os.scandir(EXTERNAL_DIR), key=lambda e: -e.stat().st_size)
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# for e in externals:
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# print(e.name, e.stat().st_size)
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print(externals[0].path)
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external = sitk.ReadImage(externals[0].path)
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# print(external.GetSize())
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# print(external.GetSpacing())
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sz = external.GetSize()
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sp = external.GetSpacing()
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outputSize = (
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round(sz[0]*sp[0]),
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round(sz[1]*sp[1]),
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round(sz[2]*sp[2]),
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)
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outputOrigin = external.GetOrigin()
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outputSpacing = (1., 1., 1.)
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outputDirection = external.GetDirection()
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mask = sitk.Resample(external, outputSize, sitk.Transform(), sitk.sitkNearestNeighbor, outputOrigin, outputSpacing, outputDirection)
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# sitk.WriteImage(mask, '0mask.nii.gz')
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# print(mask.GetSize())
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# print(mask.GetSpacing())
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lsif = sitk.LabelShapeStatisticsImageFilter()
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lsif.Execute(mask)
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if 1 not in lsif.GetLabels():
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print('strange mask... skip', root)
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return None
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boundingBox = lsif.GetBoundingBox(1)
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print(boundingBox)
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ix, iy, iz, sx, sy, sz = boundingBox
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sy = min(sy, MAX_Y)
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sz0 = min(sz, SIZE_Z-2) # will pad later
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iz0 = iz+sz-sz0
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roi = sitk.RegionOfInterest(mask, (sx, sy, sz0), (ix, iy, iz0))
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# remove frame
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ER = 10
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ER = 29
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ER = 44
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ER = 40 # remove most of it
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ER = 37 # remove most of it
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roi = sitk.BinaryErode(roi, (ER,ER,ER))
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roi2 = sitk.BinaryDilate(roi, (ER,ER,ER))
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# sitk.WriteImage(roi, '0roi.nii.gz')
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# exit()
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# print(external.GetSize())
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# print(roi.GetSize())
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#Now no shoulder, get BB again
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lsif.Execute(roi2)
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# Something wrong? 6L63PXNV
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# print(lsif.GetLabels())
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if 1 not in lsif.GetLabels():
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return None
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boundingBox = lsif.GetBoundingBox(1)
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print(boundingBox)
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ix2, iy2, iz2, sx2, sy2, sz2 = boundingBox
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# sy2 = sy
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iy2 = 0
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# iy2 = min(iy2, roi2.GetSize()[1]-sy2)
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sy2 = (sy+sy2)//2
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print((sx2, sy2, sz2), (ix2, iy2, iz2))
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roi = sitk.RegionOfInterest(roi2, (sx2, sy2, sz0), (ix2, iy2, 0))
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# roi = resize_with_pad(roi)
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# roi = sitk.FFTPad(roi)
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ypad = min(2, (SIZE_Y-sy2)//2)
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roi = sitk.ConstantPad(roi, (1,ypad,1), (1,ypad,1))
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# roi = sitk.ConstantPad(roi, (0,0,0), (0,SIZE_Y-sy2,0))
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print(external.GetSize())
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print(roi.GetSize())
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# sitk.WriteImage(external, '0external.nii.gz')
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# sitk.WriteImage(roi, '0roi.nii.gz')
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# exit()
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ct = sitk.ReadImage(CT_IMAGE,
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# sitk.sitkFloat32
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)
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CTresampled = sitk.Resample(ct, roi,
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# sitk.Transform(),
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# sitk.sitkBSpline,
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)
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rescaled = sitk.RescaleIntensity(sitk.Clamp(CTresampled,
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sitk.sitkUInt8,
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# sitk.sitkFloat32,
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0, 80))
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TV = None
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for e in sorted(os.scandir(RT_DIR), key=lambda e: e.name):
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if not e.name.startswith('TV-'):
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continue
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tv = sitk.ReadImage(e.path)
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TVresampled = sitk.Resample(tv, roi,
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sitk.Transform(),
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sitk.sitkNearestNeighbor,
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)
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if TV is None:
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TV = TVresampled
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else:
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TV = sitk.Maximum(TV, TVresampled)
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if TV is None:
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continue
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EXresampled = sitk.Resample(external, roi,
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sitk.Transform(),
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sitk.sitkNearestNeighbor,
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)
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relpath = os.path.relpath(root, PATIENTS_ROOT)
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OUT_DIR = os.path.join(OUT_ROOT, relpath)
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os.makedirs(OUT_DIR, exist_ok=True)
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CT0 = os.path.join(OUT_DIR, 'ct0.nii.gz')
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CT1 = os.path.join(OUT_DIR, 'ct1.nii.gz')
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print(CT0)
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sitk.WriteImage(CTresampled, CT0)
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sitk.WriteImage(rescaled, CT1)
|
||
|
|
sitk.WriteImage(TV+EXresampled, os.path.join(OUT_DIR, 'label.nii.gz'))
|
||
|
|
|
||
|
|
draw_sitk(rescaled, os.path.join(OUT_ROOT, '0'), '%s.png'%relpath.replace('/', '-'))
|
||
|
|
# exit()
|
||
|
|
|
||
|
|
if max(roi.GetSize()) >= SIZE_X+64: #BODY
|
||
|
|
continue
|
||
|
|
|
||
|
|
if max(roi.GetSize()) > SIZE_X:
|
||
|
|
exit()
|
||
|
|
|
||
|
|
# exit()
|
||
|
|
# continue # skip registration first
|
||
|
|
|
||
|
|
ct0 = ants.image_read(CT0)
|
||
|
|
ct1 = ants.image_read(CT1)
|
||
|
|
# fixed = ants.image_read(CT0)
|
||
|
|
for root2, dirs2, files2 in os.walk(root):
|
||
|
|
dirs2.sort()
|
||
|
|
skip = (root2==root) or ('RT' in root2.split('/'))
|
||
|
|
if skip:
|
||
|
|
continue
|
||
|
|
if root2.endswith('CT'):
|
||
|
|
modality = 'CT'
|
||
|
|
fixed=ct1
|
||
|
|
else:
|
||
|
|
modality = 'other'
|
||
|
|
fixed=ct1
|
||
|
|
print(skip, root2, modality)
|
||
|
|
relpath = os.path.relpath(root2, PATIENTS_ROOT)
|
||
|
|
out_dir = os.path.join(OUT_ROOT, relpath)
|
||
|
|
for mv in sorted(os.scandir(root2), key=lambda e: e.name):
|
||
|
|
|
||
|
|
if not mv.name.endswith('nii.gz'):
|
||
|
|
continue
|
||
|
|
if mv.name.startswith('dose'):
|
||
|
|
continue
|
||
|
|
if '_DTI_' in mv.name:
|
||
|
|
continue
|
||
|
|
if '_RTDOSE_' in mv.name:
|
||
|
|
continue
|
||
|
|
if '_ROI1.' in mv.name:
|
||
|
|
continue
|
||
|
|
# if '_swi_' in mv.name:
|
||
|
|
# continue
|
||
|
|
# if '_ph.' in mv.name:
|
||
|
|
# continue
|
||
|
|
# if '_Doses_' in mv.name:
|
||
|
|
# continue
|
||
|
|
# if '_phMag.' in mv.name:
|
||
|
|
# continue
|
||
|
|
|
||
|
|
OUT_IMG = os.path.join(out_dir, mv.name)
|
||
|
|
if os.path.isfile(OUT_IMG):
|
||
|
|
print('skip', OUT_IMG)
|
||
|
|
continue
|
||
|
|
|
||
|
|
split = mv.name[:-7].split('_')
|
||
|
|
series = None
|
||
|
|
pos = -1
|
||
|
|
while series is None:
|
||
|
|
try:
|
||
|
|
series = int(split[pos])
|
||
|
|
except:
|
||
|
|
pos -= 1
|
||
|
|
print(mv.name, series)
|
||
|
|
# if series > 99:
|
||
|
|
# continue
|
||
|
|
|
||
|
|
moving = ants.image_read(mv.path)
|
||
|
|
|
||
|
|
# skip thin images
|
||
|
|
_, _, _, _, zmin, zmax = bbox2_3D(moving.numpy())
|
||
|
|
mv_height = moving.spacing[2] * (zmax-zmin)
|
||
|
|
z_ratio = mv_height / SIZE_Z
|
||
|
|
if z_ratio < MIN_OVERLAP:
|
||
|
|
print("skip", mv.name, mv_height, z_ratio)
|
||
|
|
continue
|
||
|
|
|
||
|
|
print(mv.path, moving.view().shape, mv_height, z_ratio)
|
||
|
|
|
||
|
|
if moving.max() == 0.0:
|
||
|
|
print('Total Mass of the image was zero. Aborting here to prevent division by zero later on.')
|
||
|
|
continue
|
||
|
|
|
||
|
|
mytx = registration(ct0, ct1, moving, modality)
|
||
|
|
print(mytx['ratio'], mytx['metric'])
|
||
|
|
# if (mytx['metric'] < MIN_METRIC) and (mytx['ratio'] < MIN_OVERLAP):
|
||
|
|
# print("skip", mv.name, mytx['ratio'])
|
||
|
|
# continue
|
||
|
|
|
||
|
|
os.makedirs(out_dir, exist_ok=True)
|
||
|
|
OUT_MSK = OUT_IMG.replace('.nii.gz', '.mask.nii.gz')
|
||
|
|
OUT_JSON = OUT_IMG.replace('.nii.gz', '.json')
|
||
|
|
OUT_MAT = OUT_IMG.replace('.nii.gz', '.mat')
|
||
|
|
|
||
|
|
jj = {
|
||
|
|
'ct' : mytx['ct'],
|
||
|
|
'type' : mytx['type'],
|
||
|
|
'metric': mytx['metric'],
|
||
|
|
'ratio' : mytx['ratio'],
|
||
|
|
}
|
||
|
|
|
||
|
|
with open(OUT_JSON, 'w') as f:
|
||
|
|
json.dump(jj, f, indent=1)
|
||
|
|
|
||
|
|
shutil.copy(mytx['fwdtransforms'][0], OUT_MAT)
|
||
|
|
ants.image_write(mytx['mask'], OUT_MSK)
|
||
|
|
ants.image_write(mytx['warpedout'], OUT_IMG)
|
||
|
|
|
||
|
|
metric = mytx['metric']
|
||
|
|
|
||
|
|
metric_dir = os.path.join(OUT_ROOT, "1")
|
||
|
|
OUT_TXT = os.path.join(metric_dir, '%f-%s'%(metric, mv.name.replace('.nii.gz', '.txt')))
|
||
|
|
|
||
|
|
jj['dir'] = out_dir
|
||
|
|
|
||
|
|
os.makedirs(metric_dir, exist_ok=True)
|
||
|
|
with open(OUT_TXT, 'w') as f:
|
||
|
|
json.dump(jj, f, indent=1)
|
||
|
|
|
||
|
|
|
||
|
|
# exit()
|
||
|
|
|
||
|
|
# exit()
|
||
|
|
if roi is None:
|
||
|
|
return None
|
||
|
|
return roi.GetSize()
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/2UK7274S')
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/6L63PXNV') # Took MR for base image...:(
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/5IJBRZ4K') # Large frame ... Need erode 29 ?
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/7RC7JOLL') # Large frame ... Need erode 44 ?
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/O7IOGTPD') # Large frame ... Need erode 37 ?
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/2FHZOOLU') # Poor registration
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/3N6JVTD4') # Poor registration
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/3L6LOEER') # Poor registration, metric = 1.058
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/IUS424GK') # MASK zero after resample
|
||
|
|
# check('/mnt/1220/Public/dataset2/G4/MCVGFTEI') # CTA as based image :( still need to read base image from XML
|
||
|
|
# exit()
|
||
|
|
|
||
|
|
EXCLUDE = (
|
||
|
|
'2FHZOOLU', # CT0 is cervical
|
||
|
|
# '2KCGE3UG', #cervical, but acceptable
|
||
|
|
'53JAUWVU', #cervical
|
||
|
|
'7PS3CJUR', #cervical
|
||
|
|
'ED4NQNMK', #cervical
|
||
|
|
'IUS424GK', #strange mask
|
||
|
|
'MCVGFTEI', #CTA as base image :(
|
||
|
|
)
|
||
|
|
|
||
|
|
EXCLUDE = (
|
||
|
|
)
|
||
|
|
|
||
|
|
os.makedirs(OUT_ROOT, exist_ok=True)
|
||
|
|
d = shelve.open(SHELVE) # open -- file may get suffix added by low-level
|
||
|
|
|
||
|
|
for e in sorted(os.scandir(PATIENTS_ROOT), key=lambda e: e.name):
|
||
|
|
if e.is_dir():
|
||
|
|
# print(e.name)
|
||
|
|
if e.name in d or e.name in EXCLUDE:
|
||
|
|
print('skip', e.name)
|
||
|
|
continue
|
||
|
|
ret = check(e.path)
|
||
|
|
d[e.name] = ret
|
||
|
|
d.sync()
|
||
|
|
# exit()
|
||
|
|
d.close()
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
main()
|