Anisotropic margin for CropForeground transforms

[martaranzini]

Is your feature request related to a problem? Please describe.
The current implementation of the CropForeground and CropForegroundd transforms only accept an integer as input for margin, so all dimensions get enlarged by the same amount. This does not allow for "anisotropic" margins to be defined for the bounding box (e.g. margin=[20, 20, 5]).

The problem actually arises from the function

MONAI/monai/transforms/utils.py

Line 454 in 2e1bcef

def generate_spatial_bounding_box(

where the same margin is added to each dimension.

Describe the solution you'd like
Convert the margin variable from type int to list/tuple/array of ints and adapt the implementation accordingly.

Describe alternatives you've considered
I have modified the generate_spatial_bounding_box as follows

def generate_spatial_bounding_box_anisotropic_margin(
        img: np.ndarray,
        select_fn: Callable = lambda x: x > 0,
        channel_indices: Optional[IndexSelection] = None,
        margin: Optional[Sequence[int]] = 0,
) -> Tuple[List[int], List[int]]:
    """
    generate the spatial bounding box of foreground in the image with start-end positions.
    Users can define arbitrary function to select expected foreground from the whole image or specified channels.
    And it can also add margin to every dim of the bounding box.
    Args:
        img: source image to generate bounding box from.
        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 to all dims of the bounding box.
    """
    data = img[[*(ensure_tuple(channel_indices))]] if channel_indices is not None else img
    data = np.any(select_fn(data), axis=0)
    nonzero_idx = np.nonzero(data)

    if isinstance(margin, int):
        margin = ensure_tuple_rep(margin, len(data.shape))
    margin = [m if m > 0 else 0 for m in margin]
    assert len(data.shape) == len(margin), "defined margin has different number of dimensions than input"

    box_start = list()
    box_end = list()
    for i in range(data.ndim):
        assert len(nonzero_idx[i]) > 0, f"did not find nonzero index at spatial dim {i}"
        box_start.append(max(0, np.min(nonzero_idx[i]) - margin[i]))
        box_end.append(min(data.shape[i], np.max(nonzero_idx[i]) + margin[i] + 1))
    return box_start, box_end

and updated the CropForeground transform accordingly.

[Nic-Ma]

Hi @martaranzini ,

Thanks for your feature request.
I will try to add it soon.

Thanks.