Class CropForegroundd

monai/transforms/croppad/dictionary.py:834–932

Dictionary-based version :py:class:`monai.transforms.CropForeground`. Crop only the foreground object of the expected images. The typical usage is to help training and evaluation if the valid part is small in the whole medical image. The valid part can be determined by any field in

class CropForegroundd(Cropd):
    """
    Dictionary-based version :py:class:`monai.transforms.CropForeground`.
    Crop only the foreground object of the expected images.
    The typical usage is to help training and evaluation if the valid part is small in the whole medical image.
    The valid part can be determined by any field in the data with `source_key`, for example:
    - Select values > 0 in image field as the foreground and crop on all fields specified by `keys`.
    - Select label = 3 in label field as the foreground to crop on all fields specified by `keys`.
    - Select label > 0 in the third channel of a One-Hot label field as the foreground to crop all `keys` fields.
    Users can define arbitrary function to select expected foreground from the whole source image or specified
    channels. And it can also add margin to every dim of the bounding box of foreground object.

    This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic<lazy_resampling>`
    for more information.
    """

    def __init__(
        self,
        keys: KeysCollection,
        source_key: str,
        select_fn: Callable = is_positive,
        channel_indices: IndexSelection | None = None,
        margin: Sequence[int] | int = 0,
        allow_smaller: bool = False,
        k_divisible: Sequence[int] | int = 1,
        mode: SequenceStr = PytorchPadMode.CONSTANT,
        start_coord_key: str | None = "foreground_start_coord",
        end_coord_key: str | None = "foreground_end_coord",
        allow_missing_keys: bool = False,
        lazy: bool = False,
        **pad_kwargs,
    ) -> None:
        """
        Args:
            keys: keys of the corresponding items to be transformed.
                See also: :py:class:`monai.transforms.compose.MapTransform`
            source_key: data source to generate the bounding box of foreground, can be image or label, etc.
            select_fn: function to select expected foreground, default is to select values > 0.
            channel_indices: if defined, select foreground only on the specified channels
                of image. if None, select foreground on the whole image.
            margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.
            allow_smaller: when computing box size with `margin`, whether to allow the image edges to be smaller than the
                final box edges. If `False`, part of a padded output box might be outside of the original image, if `True`,
                the image edges will be used as the box edges. Default to `False`.
                The default value is changed from `True` to `False` in v1.5.0.
            k_divisible: make each spatial dimension to be divisible by k, default to 1.
                if `k_divisible` is an int, the same `k` be applied to all the input spatial dimensions.
            mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
                ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
                available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
                One of the listed string values or a user supplied function. Defaults to ``"constant"``.
                See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
                it also can be a sequence of string, each element corresponds to a key in ``keys``.
            start_coord_key: key to record the start coordinate of spatial bounding box for foreground.
            end_coord_key: key to record the end coordinate of spatial bounding box for foreground.
            allow_missing_keys: don&#x27;t raise exception if key is missing.
            lazy: a flag to indicate whether this transform should execute lazily or not. Defaults to False.