Reads an image (must be a format that it recognized by skimage.io.imread) and converts it into a series of niftis. The image can have an arbi

filenames must be a list of strings, each pointing to a separate 3d tiff file. One file per modality. If your data only has one imaging modal

I believe they want .nii, not .nii.gz :param source_dir: :param target_dir: :return:

output_folder is where the extracted template is :param folder_with_predictions: :param output_folder: :return:

this is preparation for ensembling :return:

does not rely on dataset.json :param folder: :return:

img must be [c, x, y] img[-1] must be the segmentation with segmentation>0 being foreground :param img: :param crop_size: :param

2TP / (2TP + FP + FN)

we now allow more than 5 splits. IMPORTANT: and fold > 4 will not be a real split but just another random 80:20 split of the data. Yo

we now allow more than 5 splits. IMPORTANT: and fold > 4 will not be a real split but just another random 80:20 split of the data. Yo

writes a summary.json to folder_with_predictions :param folder_with_gts: folder where the ground truth segmentations are saved. Must be nifti

Only vertebra that are present in the reference will be evaluated :param sitk_file_ref: :param sitk_file_test: :return:

they expect us to submit a 32 bit 3d tif image with values between 0 (100% membrane certainty) and 1 (100% non-membrane certainty). We use th

promise wants mhd :-/ :param source_dir: :param target_dir: :return:

FP / (TP + FP)

FN / (TP + FN)

FP / (FP + TN)

flips the tensor at dimension dim (mirroring!) :param x: :param dim: :return:

Args: logits: [bs; ...] targets: [bs; ...]

Args: logits: [bs; num_classes; ...] targets: [bs; ...]

target must be b, c, x, y(, z) with c=1 :param net_output: :param target: :return:

:param x: :param return_skips: if none then self.default_return_skips is used :return:

:param x: :param return_skips: if none then self.default_return_skips is used :return: