Vision core

Load medical images and represent them as MedImage and MedMask tensors.

Load images

med_img_reader

def med_img_reader(
    file_path:str | pathlib.Path | fastcore.foundation.L | list, apply_reorder:bool=False, target_spacing:list=None,
    only_tensor:bool=True, dtype:_TensorMeta=Tensor
):

Loads and preprocesses a medical image.

Args: file_path: Path to the image, or a list of paths (e.g. T1, T2) that are stacked as channels of one image, using the first channel of each file. For a list, the original image and size come from the last file. apply_reorder: Whether to reorder the data to be closest to canonical (RAS+) orientation. Defaults to False. target_spacing: Voxel spacing to resample to. Defaults to None, which keeps the original spacing. only_tensor: Whether to return only image tensor. Defaults to True. dtype: Datatype for the return value. Defaults to torch.Tensor.

Returns: The preprocessed image. Returns only the image tensor if only_tensor is True, otherwise returns original image, preprocessed image, and original size.

Basic types

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MetaResolver

def MetaResolver(
    *args, **kwargs
):

A class to bypass metaclass conflict: https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/data/batch.html

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MedBase

def MedBase(
    *args, **kwargs
):

A class that represents an image object. Metaclass casts x to this class if it is of type cls._bypass_type.

target_spacing, apply_reorder and affine_matrix are CLASS attributes (shared by MedImage/MedMask), set once via item_preprocessing (from MedDataBlock); affine_matrix is latched from the first loaded image and read by spatial augmentation. One dataset per process with uniform reorder+resample before augmentation, so all samples share an affine.

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MedImage

def MedImage(
    *args, **kwargs
):

Subclass of MedBase that represents an image object.

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MedMask

def MedMask(
    *args, **kwargs
):

Subclass of MedBase that represents a mask object.

MedBase.item_preprocessing(target_spacing=[1,1,1], apply_reorder=True)
im = MedImage.create('images/IXI002-Guys-0828-T1.nii.gz')
test_eq(type(im), MedImage)

ax = im.show(anatomical_plane=0)