# BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation

arXiv: https://arxiv.org/abs/2609.30223 · HTML: https://mlsanity.com/paper/2609.30223/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [blob loss: instance imbalance aware loss functions for semantic segmentation](https://mlsanity.com/paper/2205.08209/) (2022)
- [Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI](https://mlsanity.com/paper/2504.12527/) (2025)
- [Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021](https://mlsanity.com/paper/2208.07167/) (2022)
- [Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation](https://mlsanity.com/paper/2007.10033/) (2020)
- [Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks](https://mlsanity.com/paper/2410.18684/) (2024)
- [Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting](https://mlsanity.com/paper/2107.12978/) (2021)
- [Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation](https://mlsanity.com/paper/2511.17146/) (2025)
- [Instance Awareness of Multi-class Semantic Segmentation Loss Functions](https://mlsanity.com/paper/2604.24276/) (2026)

## Abstract (arXiv)

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.