Abstract
Common segmentation losses aggregate errors voxel-wise, so lesions influence the objective in proportion to their volume, giving small but clinically critical lesions disproportionately little weight. Instance-aware losses aim to address this mismatch by assigning each lesion its own term. However, blob loss and CC-DiceCE derive their regions solely from annotations, so false-positive components receive no instance-level term. This matters in computer-assisted review, where each false-positive component may require separate inspection, making precision and false-positive burden important alongside recall. We introduce the bidirectional connected-component loss (BiCC), which pairs annotation- and prediction-derived partitions to score predicted components on their own scale. By deriving instances from the predictions, this branch directly penalizes false-positive components regardless of their size. The balance parameter $α$ allows control over the lesion-wise precision-recall trade-off. Across five datasets with five-fold cross-validation using nnU-Net, BiCC outperforms CC-DiceCE in lesion-wise F1 on four datasets and blob loss on all five. It significantly improves over DiceCE on three datasets and matches it on two; CC-DiceCE instead loses up to 0.363 precision by favoring recall. Code is available at https://github.com/TIO-IKIM/BiCC-Loss.
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Publication details
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Cite this article
APA 7
Bouteille, L., Jonske, F., Kleesiek, J., & Jaus, A. (2026). BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation. https://omanscience.com/en/articles/bicc-bidirectional-connected-component-loss-for-instance-aware-segmentation
MLA 9
Bouteille, Luc, et al. "BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation." https://omanscience.com/en/articles/bicc-bidirectional-connected-component-loss-for-instance-aware-segmentation.
Chicago (author–date)
Bouteille, Luc, Frederic Jonske, Jens Kleesiek, and Alexander Jaus. 2026. "BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation." https://omanscience.com/en/articles/bicc-bidirectional-connected-component-loss-for-instance-aware-segmentation.
Harvard
Bouteille, L., Jonske, F., Kleesiek, J. and Jaus, A. (2026) 'BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation', Available at: https://omanscience.com/en/articles/bicc-bidirectional-connected-component-loss-for-instance-aware-segmentation.
Vancouver
Bouteille L, Jonske F, Kleesiek J, Jaus A. BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation. https://omanscience.com/en/articles/bicc-bidirectional-connected-component-loss-for-instance-aware-segmentation
IEEE
L. Bouteille, F. Jonske, J. Kleesiek, and A. Jaus, "BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation," https://omanscience.com/en/articles/bicc-bidirectional-connected-component-loss-for-instance-aware-segmentation.