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camus.py
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camus.py
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import os
from pathlib import Path
from typing import Union
import numpy as np
import SimpleITK as sitk
from tqdm import tqdm
from ascent.preprocessing.preprocessing import resample_image, resample_label
from utils import generate_dataset_json
def convert_to_nnUNet(
data_dir: Union[Path, str],
output_dir: Union[Path, str],
test: bool = False,
sequence: bool = False,
views: list = ["2CH", "4CH"],
resize: bool = False,
) -> None:
"""Convert Camus dataset to nnUNet's format.
Args:
data_dir: Path to the dataset.
output_dir: Path to the output folder to save the converted data.
test: Whether is test dataset.
sequence: Whether to convert the whole sequence or 2CH/4CH ED/ES only. (images only)
views: Views to be converted.
resize: Whether to resize images to 256x256xT.
"""
if not test:
images_out_dir = os.path.join(output_dir, "imagesTr")
labels_out_dir = os.path.join(output_dir, "labelsTr")
else:
if not resize:
images_out_dir = os.path.join(output_dir, "imagesTs")
labels_out_dir = os.path.join(output_dir, "labelsTs")
else:
images_out_dir = os.path.join(output_dir, "imagesTs256")
labels_out_dir = os.path.join(output_dir, "labelsTs256")
os.makedirs(images_out_dir, exist_ok=True)
os.makedirs(labels_out_dir, exist_ok=True)
for case in tqdm(os.listdir(data_dir)):
case_path = os.path.join(data_dir, case)
if os.listdir(case_path):
if not sequence:
for view in views:
for instant in ["ED", "ES"]:
case_identifier = f"{case}_{view}_{instant}"
image = sitk.ReadImage(os.path.join(case_path, f"{case_identifier}.mhd"))
if os.path.isfile(os.path.join(case_path, f"{case_identifier}_gt.mhd")):
label = sitk.ReadImage(
os.path.join(case_path, f"{case_identifier}_gt.mhd")
)
else:
label = None
sitk.WriteImage(
image, os.path.join(images_out_dir, f"{case_identifier}_0000.nii.gz")
)
if label is not None:
sitk.WriteImage(
label, os.path.join(labels_out_dir, f"{case_identifier}.nii.gz")
)
else:
for view in views:
case_identifier = f"{case}_{view}_sequence"
image = sitk.ReadImage(os.path.join(case_path, f"{case_identifier}.mhd"))
sitk.WriteImage(
image, os.path.join(images_out_dir, f"{case_identifier}_0000.nii.gz")
)
if os.path.isfile(os.path.join(case_path, f"{case_identifier}_gt.mhd")):
label = sitk.ReadImage(
os.path.join(case_path, f"{case_identifier}_gt.mhd")
)
else:
label = None
if resize:
ori_shape = image.GetSize()
ori_spacing = image.GetSpacing()
new_shape = [256, 256, ori_shape[-1]]
new_spacing = (
np.array(ori_spacing) * np.array(ori_shape) / np.array(new_shape)
)
image_array = sitk.GetArrayFromImage(image).transpose(2, 1, 0)
image_array = image_array[None]
resized_image_array = resample_image(image_array, new_shape, True)
image = sitk.GetImageFromArray(resized_image_array[0].transpose(2, 1, 0))
image.SetSpacing(new_spacing)
sitk.WriteImage(
image, os.path.join(images_out_dir, f"{case_identifier}_0000.nii.gz")
)
if label is not None:
if resize:
label_array = sitk.GetArrayFromImage(label).transpose(2, 1, 0)
label_array = label_array[None]
resized_label_array = resample_label(label_array, new_shape, True)
label = sitk.GetImageFromArray(
resized_label_array[0].transpose(2, 1, 0)
)
label.SetSpacing(new_spacing)
sitk.WriteImage(
label, os.path.join(labels_out_dir, f"{case_identifier}.nii.gz")
)
def convert_to_CAMUS_submission(
predictions_dir: Union[Path, str], output_dir: Union[Path, str]
) -> None:
"""Convert predictions to correct format for submission.
Args:
predictions_dir: Path to the prediction folder.
output_dir: Path to the output folder to save the converted predictions.
"""
os.makedirs(output_dir, exist_ok=True)
for case in tqdm(os.listdir(predictions_dir)):
case_path = os.path.join(predictions_dir, case)
case_identifier = case[:-7]
image = sitk.ReadImage(case_path)
sitk.WriteImage(image, os.path.join(output_dir, f"{case_identifier}.mhd"))
if __name__ == "__main__":
base = "C:/Users/ling/Downloads/training/training"
test_data = "C:/Users/ling/Downloads/testing/testing"
output_dir = "C:/Users/ling/Desktop/Thesis/REPO/ASCENT/data/CAMUS_challenge/raw"
os.makedirs(output_dir, exist_ok=True)
dataset_name = "CAMUS"
imagesTr = os.path.join(output_dir, "imagesTr")
labelsTr = os.path.join(output_dir, "labelsTr")
imagesTs = os.path.join(output_dir, "imagesTs")
labelsTs = os.path.join(output_dir, "labelsTs")
os.makedirs(imagesTr, exist_ok=True)
os.makedirs(labelsTr, exist_ok=True)
os.makedirs(imagesTs, exist_ok=True)
os.makedirs(labelsTs, exist_ok=True)
# Convert train data to nnUNet's format
convert_to_nnUNet(base, output_dir)
# Generate dataset.json
generate_dataset_json(
os.path.join(output_dir, "dataset.json"),
imagesTr,
imagesTs,
("US",),
{0: "background", 1: "LV", 2: "MYO", 3: "LA"},
dataset_name,
)
# Convert test data to nnUNet's format
convert_to_nnUNet(test_data, output_dir, sequence=False, test=True, resize=False)
# Convert predictions in Nifti format to raw/mhd
prediction_dir = "C:/Users/ling/Desktop/camus_test/inference_raw"
submission_dir = "C:/Users/ling/Desktop/camus_test/submission"
convert_to_CAMUS_submission(prediction_dir, submission_dir)