$ python scripts/prepare_aorta_segmentation.py -h
usage: prepare_2D.py [-h] [--batch-size N] [--j J] idir odir
Preprocess CT scans for ROI segmentation.
positional arguments:
idir input directory
odir output directory
optional arguments:
-h, --help show this help message and exit
--batch-size N batch size to load in RAM
--j J number of process to run simultaneously
$ python scripts/prepare_aorta_segmentation.py ../DM_Data/RIII/ ../DM_Data/RIII_test/
100%|██████████████████████████████████████████████████████████████████| 768/768 [2:16:30<00:00, 10.76s/it]$ python scripts/train_aorta_segmentation.py -h
usage: train_2D.py [-h] [--batch-size N] [--epochs E] [--split S] [--j J]
idir mdir
Train UNet over CT scans for ROI segmentation.
positional arguments:
idir input directory
mdir output model directory
optional arguments:
-h, --help show this help message and exit
--batch-size N batch size to load in RAM
--epochs E number of epochs
--split S train / test split: train = patient_ids[int(SPLIT *
len(patient_ids)):]
--j J number of process to run simultaneously
$ python scripts/train_aorta_segmentation.py ../DM_Data/RIII_test/ ../DM_Data/RIII_test_model/
Epoch 1/1
1/1 [==============================] - 4s 4s/step - loss: -0.1278 - dice_coef: 0.1278
Iteration 0/300
val dice: -0.0497503019869
--------------------------------------------------
Epoch 1/1
1/1 [==============================] - 1s 686ms/step - loss: -0.1426 - dice_coef: 0.1426
Iteration 1/300
val dice: -0.0524366907775
--------------------------------------------------$ python scripts/predict_aorta_segmentation.py -h
usage: segment_2D.py [-h] [--spacing S] [--batch-size N] [--TTA T] [--j J]
idir odir mpath
UNet inference over CT scans for ROI segmentation.
positional arguments:
idir input directory
odir output directory
mpath path to the model
optional arguments:
-h, --help show this help message and exit
--spacing S if included isotropic spacing of CT will be forced,
otherwise original spacing will be preserved
--batch-size N batch size to load in RAM
--TTA T whether to have test time augmentations, T in {0, 1, 2, 3}
--j J number of process to run simultaneously
$ python scripts/predict_aorta_segmentation.py ../DM_Data/RIII ../DM_Data/RIII_test ../DM_Data/RIII_models/unet_model --batch-size=32 --spacing=1.0
100%|████████████████████████████████████████████████████████████████████| 12/12 [03:05<00:00, 15.45s/it]
$ python scripts/extract_normals.py -h
usage: extract_normals.py [-h] [--side SIDE] [--j J] maskdir patdir odir
Extract normal planes of CT scans and predicted masks.
positional arguments:
maskdir masks input directory
patdir input directory should contains patients' CT scans
odir output directory
optional arguments:
-h, --help show this help message and exit
--side SIDE output directory
--j J number of process to run simultaneously
$ python scripts/extract_normals.py ../DM_Data/RIII_test ../DM_Data/RIII_test_patients ../DM_Data/RIII_test_planes
Iteration 1/5, patient id: 0679R3035.npy
100%|██████████████████████████████████████████████████████████████████| 130/130 [02:01<00:00, 1.07it/s]
Iteration 2/5, patient id: 0304R3039.npy
100%|██████████████████████████████████████████████████████████████████| 211/211 [02:08<00:00, 1.64it/s]
Iteration 3/5, patient id: 0303R3023.npy
10%|██████████████████████████████████████████████████████████████████| 149/149 [01:06<00:00, 2.19it/s]
$ python scripts/prepare_valve_segmentation.py -h
usage: prepare_valve_segmentation.py [-h] [--n N] idir mdir odir
Prepare dataset for valve segmentation.
positional arguments:
idir input directory (should contains zis.npy and prods.npy)
mdir directory with valve masks
odir output directory
optional arguments:
-h, --help show this help message and exit
--n N maximum number of samples to be processed
$ python scripts/prepare_valve_segmentation.py ../DM_Data/RIII /home/ubuntu/edata/ ../DM_Data/RIII_test --n=4
100%|██████████████████████████████████████████████████████████████████████| 4/4 [00:44<00:00, 11.03s/it]
$ python scripts/train_valve_segmentation.py -h
usage: train_valve_segmentation.py [-h] [--epochs E] idir mdir mpath
Train valve segmentation model.
positional arguments:
idir input directory (should contains folders with zis.npy and
prods.npy)
mdir directory with prepared data (should contains folders with
mask_*.npy)
mpath path to the model
optional arguments:
-h, --help show this help message and exit
--epochs E maximum number of epochs to be trained
$ python scripts/train_valve_segmentation.py ../DM_Data/RIII ~/edata/patchs/ ../DM_Data/RIII_test_model/valve --epochs=2
Epoch 1/2
14/14 [==============================] - 6s 401ms/step - loss: 1.0654 - binary_crossentropy: 0.3117 - dice_coef: 0.2463 - val_loss: 0.7435 - val_binary_crossentropy: 0.0818 - val_dice_coef: 0.3383
Epoch 2/2
14/14 [==============================] - 3s 206ms/step - loss: 0.7789 - binary_crossentropy: 0.1465 - dice_coef: 0.3677 - val_loss: 0.7318 - val_binary_crossentropy: 0.0872 - val_dice_coef: 0.3554$ python scripts/predict_valve_segmentation.py -h
usage: predict_valve_segmentation.py [-h] [--n N] idir odir mpath
Valve segmentation model inference over prepared dataset.
positional arguments:
idir directory with prepared data (should contains mask_*.npy)
odir output directory
mpath path to the model
optional arguments:
-h, --help show this help message and exit
--n N maximum number of epochs to be trained
$ python scripts/predict_valve_segmentation.py ~/edata/patches ~/edata/valve_output ~/edata/xception_valve --n=4
100%|██████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 4.16it/s]
$ python scripts/prepare_features.py -h
usage: prepare_features.py [-h] [--labels_path LABELS_PATH]
[--exclude_paths EXCLUDE_PATHS] [--n N]
idir zpdir valve_path odir
Prepare dataset for valve segmentation.
positional arguments:
idir input directory
zpdir directory should contains zis.npy and prods.npy
valve_path path to the valve.csv
odir output directory
optional arguments:
-h, --help show this help message and exit
--labels_path LABELS_PATH
path to the REPRISE III Sizes.xlsx
--exclude_paths EXCLUDE_PATHS
path to the pickled version of excluded paths
--n N maximum number of samples to be processed
$ python scripts/prepare_features.py ~/edata/ ~/cdata/DM_Data/RIII valve.csv ~/edata/features.csv --labels_path=../DM_Data/REPRISE\ III\ Sizes.xlsx --exclude_paths=exclude_paths --n=3
100%|██████████████████████████████████████████████████████████████████████| 3/3 [01:03<00:00, 21.28s/it]from sklearn.model_selection import LeaveOneOut
from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier
import pandas as pd
from tqdm import tqdm
import pickle
features = pd.read_csv('features')
features = features.drop(['Unnamed: 0'], axis=1)
labels = features[['class']]
for label in labels['class'].unique():
labels[label] = features['class'] == label
labels = labels.drop(['class'], axis=1).values
features = features.drop(['class', 'seriesuid'], axis=1).values
loo = LeaveOneOut()
predicted = dict()
gt = list()
clfs = [
GradientBoostingClassifier(n_estimators=3, max_depth=2, random_state=10)
]
for clf in clfs:
predicted[str(clf.__class__)] = list()
for split, lo in tqdm(loo.split(features)):
clf.fit(features[split], np.argmax(labels[split], axis=1))
predicted[str(clf.__class__)].append(clf.predict_proba(features[lo]))
for i, clf in enumerate(clfs):
pred = np.array(predicted[str(clf.__class__)])
pred = np.argmax(pred, axis=-1)
gt = np.argmax(labels, axis=-1)
print((len(labels) - (np.abs(np.squeeze(pred) - gt) > 0).sum()) / len(labels))
pickle.dump(clfs, open('../clfs', 'wb'))