MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models

2026-06-04

AI Summary

This study addresses the frequent oversight in existing medical image segmentation methods regarding the fundamental influence of dataset characteristics on model design, which often leads to a mismatch between architectural choices and task requirements. To bridge this gap, the authors propose the Medical Segmentation Dataset Knowledge Card (MS-DKC) framework—a novel paradigm that systematically constructs a five-dimensional knowledge representation encompassing imaging/acquisition properties, morphological features, supervision schemes, contextual dependencies, and deployment risks. This framework explicitly links dataset attributes to failure modes, design priors, and risk-alignment criteria, enabling traceable, data-driven model customization. Experiments on DRIVE, ISIC2018, and ACDC demonstrate that models designed under the MS-DKC paradigm—such as MS-DKC-AttNextTopo-VCSF-NoAug, achieving a Dice score of 0.8872—significantly outperform generic architectures, thereby validating the efficacy and superiority of the proposed approach.

Abstract

Medical image segmentation is often framed as a search for stronger architectures, but this can obscure a more fundamental question: what does the dataset require from the model? In medical imaging, this requirement is shaped by foreground occupancy, morphology, boundary ambiguity, topology sensitivity, annotation quality, acquisition variation, and operating point. This paper introduces the Medical Segmentation Dataset Knowledge Card (MS-DKC), a framework for making these factors explicit. MS-DKC records dataset evidence through image/acquisition, morphology, supervision, context-dependence, and deployment-risk descriptors. These descriptors are mapped to failure modes, design priors, and risk-aligned criteria, making segmentation design more traceable than architecture-first comparison. We evaluate MS-DKC on DRIVE, ISIC2018, and ACDC, representing distinct regimes. DRIVE contains sparse, thin, branching vessels, favoring detail-preserving models, sensitivity-aware optimization, threshold analysis, and topology-aware metrics. DKC-TNet-v2 achieved Dice 0.8044 and IoU 0.6730 with 35103 parameters, while SA-UNetv2-DKC-AmbRef reached Dice 0.8141, IoU 0.6865, sensitivity 0.8265, specificity 0.9804, and AUC 0.9853. ISIC2018 involves compact but appearance-variable lesions; validation-constrained score-function selection on Att-Next-Topo/ATTNext produced MS-DKC-AttNextTopo-VCSF-NoAug with Dice 0.8872, IoU 0.8214, precision 0.9173, Boundary F1 0.4878, and ASSD 4.13, while plausible additions failed to improve the risk-aligned profile. ACDC provides a multi-class cardiac case, where MS-DKC recommends four-class softmax segmentation, class-balanced Dice/CE supervision, and class-wise surface evaluation. Overall, the results support dataset-conditioned design: different datasets require different priors, operating points, and evidence before a model can be judged appropriate.

Problem

• medical image segmentation
• dataset requirements
• model design
• annotation quality
• acquisition variation

Innovation

• Dataset Knowledge Card
• Medical Image Segmentation
• Risk-aligned Design
• Morphology-aware Modeling
• Failure Mode Mapping