# Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks

arXiv: https://arxiv.org/abs/2410.18684 · HTML: https://mlsanity.com/paper/2410.18684/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [Understanding metric-related pitfalls in image analysis validation](https://mlsanity.com/paper/2302.01790/) (2023)

## Built on by (most cited first, 8 of 8)

- [Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation](https://mlsanity.com/paper/2511.17146/) (2025)
- [The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT unicodex2013 Multitracer Multicenter Generalization](https://mlsanity.com/paper/2605.05775/) (2026)
- [Instance Awareness of Multi-class Semantic Segmentation Loss Functions](https://mlsanity.com/paper/2604.24276/) (2026)
- [Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation](https://mlsanity.com/paper/2605.31094/) (2026)
- [GRASPing Anatomy to Improve Pathology Segmentation](https://mlsanity.com/paper/2508.03374/) (2025)
- [SynSeg: Feature Synergy for Multi-Category Contrastive Learning in End-to-End Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2508.06115/) (2025)
- [Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI](https://mlsanity.com/paper/2604.08015/) (2026)
- [BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation](https://mlsanity.com/paper/2609.30223/) (2026)

## Abstract (arXiv)

We present Connected-Component~(CC)-Metrics, a novel semantic segmentation evaluation protocol, targeted to align existing semantic segmentation metrics to a multi-instance detection scenario in which each connected component matters. We motivate this setup in the common medical scenario of semantic metastases segmentation in a full-body PET/CT. We show how existing semantic segmentation metrics suffer from a bias towards larger connected components contradicting the clinical assessment of scans in which tumor size and clinical relevance are uncorrelated. To rebalance existing segmentation metrics, we propose to evaluate them on a per-component basis thus giving each tumor the same weight irrespective of its size. To match predictions to ground-truth segments, we employ a proximity-based matching criterion, evaluating common metrics locally at the component of interest. Using this approach, we break free of biases introduced by large metastasis for overlap-based metrics such as Dice or Surface Dice. CC-Metrics also improves distance-based metrics such as Hausdorff Distances which are uninformative for small changes that do not influence the maximum or 95th percentile, and avoids pitfalls introduced by directly combining counting-based metrics with overlap-based metrics as it is done in Panoptic Quality.