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

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Alexander Jaus , Constantin Seibold , Simon Reiß , Zdravko Marinov , Keyi Li , Ziqi Ye , Stefan Krieg , Jens Kleesiek , Rainer Stiefelhagen ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1609/aaai.v39i4.32408

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.

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Segmentation Fault

Computer Science

Artificial Intelligence

Data Mining

Physics

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Segmentation Component (thermodynamics) Measure (data warehouse) Computer science Artificial intelligence Natural language processing Data mining Physics Thermodynamics

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Type: article
Language: en
Landing Page: https://doi.org/10.1609/aaai.v39i4.32408
PDF: https://ojs.aaai.org/index.php/AAAI/article/download/32408/34563
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DOI: https://doi.org/10.1609/aaai.v39i4.32408

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

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Language: en

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Publication year: 2025

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Authors: Alexander Jaus, Constantin Seibold, Simon Reiß, Zdravko Marinov, Keyi Li, Ziqi Ye, Stefan Krieg, Jens Kleesiek, Rainer Stiefelhagen

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Landing page: https://doi.org/10.1609/aaai.v39i4.32408

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Concepts: Segmentation, Component (thermodynamics), Measure (data warehouse), Computer science, Artificial intelligence, Natural language processing, Data mining, Physics, Thermodynamics

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