基于未配对经阴道超声（TVUS）原型先验的子宫内膜异位症跨模态MRI卵巢分割

中文摘要 AI 辅助

经阴道超声（TVUS）和磁共振成像（MRI）为子宫内膜异位症图像分析提供互补信息，但现有研究主要聚焦于单模态分析或疾病分类，跨模态卵巢分割领域尚未得到充分探索。本研究针对MRI中卵巢因目标尺寸小、与周围盆腔结构边界模糊导致的分割难度提升问题，提出一种适用于TVUS和MRI跨模态卵巢分割的双分支框架。具体而言，通过适配MedSAM3并结合TVUS衍生的原型库，旨在对齐两种模态间解剖结构一致的特征表示。在子宫内膜异位症相关的TVUS和MRI数据集上开展大量实验，观察到所提双分支方法相较于多种最先进方法，在定量和定性指标上均实现了超过5个百分点的提升。此外，消融研究验证了原型库等各组件的贡献，以及在源TVUS域进行热身预训练的重要性。

英文摘要

Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis, yet existing studies mainly focus on single-modality analysis or disease classification, leaving cross-modal ovarian segmentation largely unexplored. In this work, to tackle the increased difficulty of ovary segmentation in MRI due to ovaries' small target size and ambiguous boundaries with surrounding pelvic structures, we propose a dual branch framework for ovary segmentation across TVUS and MRI. More specifically, by adapting MedSAM3 with TVUS-derived prototype bank, we aim to align anatomically consistent feature representations across both modalities. Extensive experiments are conducted on endometriosis-related TVUS and MRI datasets. We observe quantitative and qualitative improvements of over 5 percentage points for the proposed dual-branch approach compared with multiple state-of-the-art methods. Furthermore, our ablation study shows the contribution of individual components such as the prototype bank and the importance of warm-up pretraining in the source TVUS domain.

发表机构

• Center for AI and Data Science (CAIDAS), Julius-Maximilians-Universität Würzburg（维尔茨堡大学人工智能与数据科学中心）
• Institute for Computational Imaging and AI in Medicine (CompAI), Technical University of Munich（慕尼黑工业大学计算成像与医学人工智能研究所）
• Pattern Recognition Lab, Friedrich-Alexander Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学模式识别实验室）
• Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学生物医学工程人工智能系）

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