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arXiv · 2506.24003

ShapeKit

Junqi Liu·Dongli He·Wenxuan Li·Ningyu Wang·Alan L. Yuille·Zongwei Zhou
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Abstract

In this paper, we present a practical approach to improve anatomical shape accuracy in whole-body medical segmentation. Our analysis shows that a shape-focused toolkit can enhance segmentation performance by over 8%, without the need for model re-training or fine-tuning. In comparison, modifications to model architecture typically lead to marginal gains of less than 3%. Motivated by this observation, we introduce ShapeKit, a flexible and easy-to-integrate toolkit designed to refine anatomical shapes. This work highlights the underappreciated value of shape-based tools and calls attention to their potential impact within the medical segmentation community.

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Junqi Liu, Dongli He, Wenxuan Li, Ningyu Wang, Alan L. Yuille, Zongwei Zhou. 2025-06-30. ShapeKit. https://arxiv.org/abs/2506.24003

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