CropForegroundd on ROI with text

[pure-rgb]

Describe the bug

CropForegroundd works very well for mammography images that contain only the breast and background. However, some mammograms also contain foreground text or markers (e.g., laterality labels, acquisition text, scanner annotations). Since these annotations are non-zero pixels, they are detected as foreground by select_fn, causing the computed bounding box to include them and resulting in a larger crop than desired.

To Reproduce

input:

import numpy as np
from monai.transforms import CropForegroundd

transform = CropForegroundd(
    keys=["image"],
    source_key="image",
)

output = transform({"image": input_array})
cropped = output["image"]
cropped = np.transpose(cropped, (1, 2, 0))

Output returns same as input with text label.

Expected behavior

Maybe only return the breast region.

[MDSALMANSHAMS]

Thanks for the report. This is expected behavior - CropForeground computes the bounding box over all foreground pixels selected by select_fn, so disconnected text/markers (laterality labels, scanner annotations) get pulled into the box alongside the breast.

A clean, backward-compatible fix would be an opt-in flag to keep only the largest connected component of the foreground mask before computing the bbox - e.g. CropForeground(..., select_largest_component=False) (default False preserves current behavior). MONAI already has get_largest_connected_component_mask in monai/transforms/utils.py, so compute_bounding_box could apply it to the selected mask when the flag is set, before calling generate_spatial_bounding_box.

This would handle the mammography case (keep the breast, drop separate text regions) without breaking existing users. Does this direction sound right to the maintainers? If so, I'd be happy to open a PR (with tests) - could this be labeled/confirmed as an enhancement?