用多查询统一自动与交互分割，提升心脏MRI精准度
VerSe: Integrating Multiple Queries as Prompts for Versatile Cardiac MRI Segmentation
- 用对象与点击查询联合学习作为统一提示
- 在心脏MRI上达到更高分割准确率，减少人工修正
- 适合需要高效精准分割的医学影像研究者
尽管基于学习的图像分割方法取得了进展，从磁共振成像（MRI）中准确分割心脏结构仍是关键挑战。现有自动分割方法虽有潜力，但在心基底和心尖等复杂区域仍需专家大量手动修正。近期交互式分割方法虽引入人机协同，但依赖点击提示，效率低，尤其在3D心脏MRI数据上。为此，我们提出VerSe框架，通过多查询统一自动与交互分割。核心创新在于联合学习对象查询与点击查询，作为共享分割主干的提示。VerSe支持仅用对象查询的全自动分割，也可在需要时通过点击查询进行掩码精修。所提出的集成提示机制在心脏MRI及分布外医学影像数据集上均显著优于现有方法，性能与效率双提升。代码已开源：https://github.com/bangwayne/Verse。
原文摘要 · Abstract (English)
Despite the advances in learning-based image segmentation approach, the accurate segmentation of cardiac structures from magnetic resonance imaging (MRI) remains a critical challenge. While existing automatic segmentation methods have shown promise, they still require extensive manual corrections of the segmentation results by human experts, particularly in complex regions such as the basal and apical parts of the heart. Recent efforts have been made on developing interactive image segmentation methods that enable human-in-the-loop learning. However, they are semi-automatic and inefficient, due to their reliance on click-based prompts, especially for 3D cardiac MRI volumes. To address these limitations, we propose VerSe, a Versatile Segmentation framework to unify automatic and interactive segmentation through mutiple queries. Our key innovation lies in the joint learning of object and click queries as prompts for a shared segmentation backbone. VerSe supports both fully automatic segmentation, through object queries, and interactive mask refinement, by providing click queries when needed. With the proposed integrated prompting scheme, VerSe demonstrates significant improvement in performance and efficiency over existing methods, on both cardiac MRI and out-of-distribution medical imaging datasets. The code is available at https://github.com/bangwayne/Verse.
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