用形状优化工具提升全身医学分割准确率，无需重训练。
ShapeKit
- 引入ShapeKit工具包，通过后处理优化解剖形状。
- 分割准确率提升超8%，远超模型结构调整的3%以内收益。
- 适合追求精度但无法调参的医疗影像研究者。
本文提出一种实用方法，用于提升全身医学图像分割中的解剖形状准确性。分析表明，采用专注形状的工具包可使分割性能提升超过8%，且无需模型重训练或微调。相比之下，模型架构修改通常仅带来小于3%的微小增益。受此启发，我们推出了ShapeKit——一个灵活、易集成的工具包，专门用于精细化调整解剖形状。本工作强调了基于形状的工具未被充分重视的价值，并呼吁其在医学分割领域发挥更大作用。
原文摘要 · Abstract (English)
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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