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ShapeKit
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AI
DOI：10.1007/978-3-032-06774-6_4.png)
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Abstract
En 中文
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. We evaluate our method on two large-scale, multi-institutional CT scan datasets with expert-verified annotations. This work highlights the underappreciated value of shape-based tools and calls attention to their potential impact within the medical segmentation community. ShapeKit is available at https://github.com/BodyMaps/ShapeKit.
Keywords:
Shapes
Anatomical Structures
Quality Control
Toolkit
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