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import os, sys |
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currentdir = os.path.dirname(os.path.realpath(__file__)) |
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parentdir = os.path.dirname(currentdir) |
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sys.path.append(parentdir) |
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import nibabel as nib |
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import re |
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import medpy.metric as medpy_metrics |
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import pandas as pd |
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import numpy as np |
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from tqdm import tqdm |
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import multiprocessing as mp |
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IMG_DIRECTORY = '/mnt/EncryptedData1/Laparoscopy/OSLO_COMET_dataset/OSLO_COMET_CT/Volumes_nii/test_set' |
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VES_SEG_DIRECTORY = '/mnt/EncryptedData1/Laparoscopy/OSLO_COMET_dataset/OSLO_COMET_CT/Vessels' |
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PAR_SEG_DIRECTORY = '/mnt/EncryptedData1/Laparoscopy/OSLO_COMET_dataset/OSLO_COMET_CT/Parenchyma' |
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LM_SEG_DIRECTORY = '/mnt/EncryptedData1/Laparoscopy/OSLO_COMET_dataset/OSLO_COMET_CT/LiverMask/test_set' |
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nii_FILENAME_PATTERN = '(.*)_CT.nii' |
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LV_VESSEL_FILENAME_PATTERN = '(.*)-vessels.nii' |
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LV_PARENCHYMA_FILENAME_PATTERN = '(.*)-livermask.nii' |
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OUT_DIRECTORY = '/mnt/EncryptedData1/Users/javier/ext_datasets/COMET_ai_vs_human' |
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def process_group(file_list): |
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img_file, par_file, ves_file, lm_par_file, lm_ves_file = file_list |
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img_num = int(re.match(nii_FILENAME_PATTERN, os.path.split(img_file)[-1])[1]) |
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human_par = nib.load(par_file) |
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header = human_par.header |
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human_par = np.asarray(human_par.dataobj) |
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human_ves = np.asarray(nib.load(ves_file).dataobj) |
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lm_par = np.asarray(nib.load(lm_par_file).dataobj) |
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lm_ves = np.asarray(nib.load(lm_ves_file).dataobj) |
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lm_ves[lm_ves > 0] = 1 |
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dsc_par = medpy_metrics.dc(human_par, lm_par) |
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dsc_ves = medpy_metrics.dc(human_ves, lm_ves) |
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hd_par = medpy_metrics.hd(human_par, lm_par, voxelspacing=header['pixdim'][1:4]) |
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hd_ves = medpy_metrics.hd(human_ves, lm_ves, voxelspacing=header['pixdim'][1:4]) |
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hd95_par = medpy_metrics.hd95(human_par, lm_par, voxelspacing=header['pixdim'][1:4]) |
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hd95_ves = medpy_metrics.hd95(human_ves, lm_ves, voxelspacing=header['pixdim'][1:4]) |
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return img_num, dsc_par, dsc_ves, hd_par, hd_ves, hd95_par, hd95_ves |
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if __name__ == '__main__': |
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img_list = [os.path.join(IMG_DIRECTORY, f) for f in os.listdir(IMG_DIRECTORY) if f.endswith('.nii')] |
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img_list.sort() |
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ves_seg_list = [os.path.join(VES_SEG_DIRECTORY, os.path.split(f)[-1]+'.gz') for f in img_list] |
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ves_seg_list.sort() |
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par_seg_list = [os.path.join(PAR_SEG_DIRECTORY, os.path.split(f)[-1]+'.gz') for f in img_list] |
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par_seg_list.sort() |
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lm_ves_seg_list = [os.path.join(LM_SEG_DIRECTORY, f) for f in os.listdir(LM_SEG_DIRECTORY) if f.endswith('-vessels.nii')] |
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lm_ves_seg_list.sort() |
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lm_par_seg_list = [os.path.join(LM_SEG_DIRECTORY, f) for f in os.listdir(LM_SEG_DIRECTORY) if f.endswith('-livermask.nii')] |
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lm_par_seg_list.sort() |
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os.makedirs(OUT_DIRECTORY, exist_ok=True) |
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zip_lists = zip(img_list, par_seg_list, ves_seg_list, lm_par_seg_list, lm_ves_seg_list) |
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mets = ['DSC', 'DSC', 'HD', 'HD', 'H95', 'H95'] |
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segs = ['Parenchyma', 'Vessels', 'Parenchyma', 'Vessels', 'Parenchyma', 'Vessels'] |
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cols = list(zip(*[mets, segs])) |
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idx = pd.MultiIndex.from_tuples(cols, names=['Metrics', 'Labels']) |
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df = pd.DataFrame(index=idx) |
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print('\nLaunching processes...') |
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with mp.Pool(11, maxtasksperchild=1) as p: |
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results = p.map_async(process_group, zip_lists) |
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for v in results.get(): |
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df[v[0]] = v[1:] |
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print('\nResults...') |
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print(df) |
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print('\nSummary...') |
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print(df.describe()) |
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df.to_csv('comparison.csv', sep=';') |
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