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# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
from batchgenerators.utilities.file_and_folder_operations import *
import shutil
import numpy as np
from numpy.random.mtrand import RandomState
import subprocess
from multiprocessing import pool
import pandas as pd
def get_mnms_data(data_root):
files_raw = []
files_gt = []
for r, dirs, files in os.walk(data_root):
for f in files:
if f.endswith('nii.gz'):
file_path = os.path.join(r, f)
if '_gt' in f:
files_gt.append(file_path)
else:
files_raw.append(file_path)
return files_raw, files_gt
def generate_filename_for_nnunet(pat_id, ts, pat_folder=None, add_zeros=False, vendor=None, centre=None, mode='mnms',
data_format='nii.gz'):
if not vendor or not centre:
if add_zeros:
filename = "{}_{}_0000.{}".format(pat_id, str(ts).zfill(4), data_format)
else:
filename = "{}_{}.{}".format(pat_id, str(ts).zfill(4), data_format)
else:
if mode == 'mnms':
if add_zeros:
filename = "{}_{}_{}_{}_0000.{}".format(pat_id, str(ts).zfill(4), vendor, centre, data_format)
else:
filename = "{}_{}_{}_{}.{}".format(pat_id, str(ts).zfill(4), vendor, centre, data_format)
else:
if add_zeros:
filename = "{}_{}_{}_{}_0000.{}".format(vendor, centre, pat_id, str(ts).zfill(4), data_format)
else:
filename = "{}_{}_{}_{}.{}".format(vendor, centre, pat_id, str(ts).zfill(4), data_format)
if pat_folder:
filename = os.path.join(pat_folder, filename)
return filename
def select_annotated_frames_mms(data_folder, out_folder, add_zeros=False, mode='mnms', df_path="/media/full/tera2/data/challenges/mms/Training-corrected_original/M&Ms Dataset Information.xlsx"):
table = pd.read_excel(df_path, index_col='External code')
for idx in table.index:
ed = table.loc[idx, 'ED']
es = table.loc[idx, 'ES']
vendor = table.loc[idx, 'Vendor']
centre = table.loc[idx, 'Centre']
if vendor != "C":
# generate old filename (w/o vendor and centre)
filename_ed_original = generate_filename_for_nnunet(pat_id=idx, ts=ed, pat_folder=data_folder,
vendor=None, centre=None, add_zeros=False)
filename_es_original = generate_filename_for_nnunet(pat_id=idx, ts=es, pat_folder=data_folder,
vendor=None, centre=None, add_zeros=False)
# generate new filename with vendor and centre
filename_ed = generate_filename_for_nnunet(pat_id=idx, ts=ed, pat_folder=out_folder,
vendor=vendor, centre=centre, add_zeros=add_zeros, mode=mode)
filename_es = generate_filename_for_nnunet(pat_id=idx, ts=es, pat_folder=out_folder,
vendor=vendor, centre=centre, add_zeros=add_zeros, mode=mode)
shutil.copy(filename_ed_original, filename_ed)
shutil.copy(filename_es_original, filename_es)
def create_custom_splits_for_experiments(task_path):
data_keys = [i[:-4] for i in
subfiles(os.path.join(task_path, "nnUNetData_plans_v2.1_2D_stage0"),
join=False, suffix='npz')]
existing_splits = os.path.join(task_path, "splits_final.pkl")
splits = load_pickle(existing_splits)
splits = splits[:5] # discard old changes
unique_a_only = np.unique([i.split('_')[0] for i in data_keys if i.find('_A_') != -1])
unique_b_only = np.unique([i.split('_')[0] for i in data_keys if i.find('_B_') != -1])
num_train_a = int(np.round(0.8 * len(unique_a_only)))
num_train_b = int(np.round(0.8 * len(unique_b_only)))
p = RandomState(1234)
idx_a_train = p.choice(len(unique_a_only), num_train_a, replace=False)
idx_b_train = p.choice(len(unique_b_only), num_train_b, replace=False)
identifiers_a_train = [unique_a_only[i] for i in idx_a_train]
identifiers_b_train = [unique_b_only[i] for i in idx_b_train]
identifiers_a_val = [i for i in unique_a_only if i not in identifiers_a_train]
identifiers_b_val = [i for i in unique_b_only if i not in identifiers_b_train]
# fold 5 will be train on a and eval on val sets of a and b
splits.append({'train': [i for i in data_keys if i.split("_")[0] in identifiers_a_train],
'val': [i for i in data_keys if i.split("_")[0] in identifiers_a_val] + [i for i in data_keys if
i.split("_")[
0] in identifiers_b_val]})
# fold 6 will be train on b and eval on val sets of a and b
splits.append({'train': [i for i in data_keys if i.split("_")[0] in identifiers_b_train],
'val': [i for i in data_keys if i.split("_")[0] in identifiers_a_val] + [i for i in data_keys if
i.split("_")[
0] in identifiers_b_val]})
# fold 7 train on both, eval on both
splits.append({'train': [i for i in data_keys if i.split("_")[0] in identifiers_b_train] + [i for i in data_keys if i.split("_")[0] in identifiers_a_train],
'val': [i for i in data_keys if i.split("_")[0] in identifiers_a_val] + [i for i in data_keys if
i.split("_")[
0] in identifiers_b_val]})
save_pickle(splits, existing_splits)
def split_4d_nii(nii_path, split_folder, pat_name=None, add_zeros=False):
# create temporary folder in which the 3d+t file will be split into many 3d files
temp_base = os.path.dirname(nii_path)
temp_location = os.path.join(temp_base, 'tmp')
if not os.path.isdir(temp_location):
os.mkdir(temp_location)
os.chdir(temp_location)
if not os.path.isdir(split_folder):
os.mkdir(split_folder)
_ = subprocess.call(['fslsplit', nii_path])
# rename files so that the patient's ID is in the filename
file_list = [f for f in os.listdir(temp_location) if os.path.isfile(f)]
file_list = sorted(file_list)
if not pat_name:
pat_name = os.path.basename(os.path.dirname(nii_path))
for ts, temp_file in enumerate(file_list):
# get time
time_step = temp_file.split('.')[0][3:]
# make sure the time step is a number. Otherwise trust in pythons sort algorithm
try:
int(time_step)
except:
time_step = ts
# change filename AND location -> move files
if add_zeros:
new_file_name = '{}_{}_0000.nii.gz'.format(pat_name, time_step)
else:
new_file_name = '{}_{}.nii.gz'.format(pat_name, time_step)
os.rename(os.path.join(temp_location, temp_file),
os.path.join(split_folder, new_file_name))
os.rmdir(temp_location)
def split_4d_parallel(args):
nii_path, split_folder, pat_name = args
split_4d_nii(nii_path, split_folder, pat_name)
def split_4d_for_all_pat(files_paths, split_folder):
p = pool.Pool(8)
p.map(split_4d_parallel,
zip(files_paths, [split_folder] * len(files_paths), [None] * len(files_paths)))
if __name__ == "__main__":
task_name = "Task114_heart_MNMs"
train_dir = "/media/full/97d8d6e1-1aa1-4761-9dd1-fc6a62cf6264/nnUnet_raw/nnUNet_raw_data/{}/imagesTr".format(task_name)
test_dir = "/media/full/97d8d6e1-1aa1-4761-9dd1-fc6a62cf6264/nnUnet_raw/nnUNet_raw_data/{}/imagesTs".format(task_name)
#out_dir='/media/full/tera2/output_nnUNet/preprocessed_data/Task114_heart_mnms'
out_dir='/media/full/97d8d6e1-1aa1-4761-9dd1-fc6a62cf6264/tmp'
# train
all_train_files = [os.path.join(train_dir, x) for x in os.listdir(train_dir)]
# test
all_test_files = [os.path.join(test_dir, x) for x in os.listdir(test_dir)]
data_root = '/media/full/97d8d6e1-1aa1-4761-9dd1-fc6a62cf6264/data/challenges/mms/Training-corrected_original/Labeled'
files_raw, files_gt = get_mnms_data(data_root=data_root)
split_path_raw ='/media/full/97d8d6e1-1aa1-4761-9dd1-fc6a62cf6264/data/challenges/mms/temp_split_raw'
split_path_gt ='/media/full/97d8d6e1-1aa1-4761-9dd1-fc6a62cf6264/data/challenges/mms/temp_split_gt'
maybe_mkdir_p(split_path_raw)
maybe_mkdir_p(split_path_gt)
split_4d_for_all_pat(files_raw, split_path_raw)
split_4d_for_all_pat(files_gt, split_path_gt)
out_dir = '/media/full/97d8d6e1-1aa1-4761-9dd1-fc6a62cf6264/nnUnet_raw/nnUNet_raw_data/{}/'.format(task_name)
maybe_mkdir_p(join(out_dir, "imagesTr"))
maybe_mkdir_p(join(out_dir, "imagesTs"))
maybe_mkdir_p(join(out_dir, "labelsTr"))
imagesTr_path = os.path.join(out_dir, "imagesTr")
labelsTr_path = os.path.join(out_dir, "labelsTr")
select_annotated_frames_mms(split_path_raw, imagesTr_path, add_zeros=True)
select_annotated_frames_mms(split_path_gt, labelsTr_path, add_zeros=False)
labelsTr = subfiles(labelsTr_path)
json_dict = OrderedDict()
json_dict['name'] = "M&Ms"
json_dict['description'] = "short axis cardiac cine MRI segmentation"
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "Campello, Víctor M. et al.: Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation. In preparation."
json_dict['licence'] = "see M&Ms challenge"
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "MRI",
}
# labels differ for ACDC challenge
json_dict['labels'] = {
"0": "background",
"1": "LVBP",
"2": "LVM",
"3": "RV"
}
json_dict['numTraining'] = len(labelsTr)
json_dict['numTest'] = 0
json_dict['training'] = [{'image': "./imagesTr/%s" % i.split("/")[-1], "label": "./labelsTr/%s" % i.split("/")[-1]} for i in
labelsTr]
json_dict['test'] = []
save_json(json_dict, os.path.join(out_dir, "dataset.json"))
# then preprocess data and plan training.
# run in terminal
# > nnUNet_plan_and_preprocess -t <TaskID> --verify_dataset_integrity
# start training and stop it immediately to get a split.pkl file
# > nnUNet_train 2d nnUNetTrainerV2_MMS <TaskID> 0
#
# then create custom splits as used for the final M&Ms submission
#
split_file_path = '/media/full/97d8d6e1-1aa1-4761-9dd1-fc6a62cf6264/output_nnUNet/preprocessed_data/{}/'.format(task_name)
create_custom_splits_for_experiments(split_file_path)
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