发表机构
UNC at Chapel Hill（北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出TPUS框架，利用拓扑信息与提示条件，实现超声和MRI上子宫多结构通用分割，在两类数据集上取得优于基线的Dice分数。
AI 中文摘要
子宫的多结构分割对于子宫疾病的计算机辅助筛查、诊断和治疗规划具有重要意义，其中超声和MRI提供了互补的临床信息。然而，由于这两种模态在图像外观、解剖上下文、空间分辨率和标签空间上存在显著差异，开发跨模态的统一模型具有挑战性。此外，现有数据集通常定义不同的分割目标，使得联合学习困难，并可能导致异构任务间的负迁移。为此，我们提出了一种基于拓扑信息与提示条件的通用分割（TPUS）框架，用于在超声和MRI上分割多个子宫结构。TPUS引入了一个基于图的多数据集主干，包含模态特定的茎和模态共享的基于图的编码器-解码器，以支持模态敏感输入适应、结构特征推理以及跨异构子宫分割任务的联合表示学习。此外，TPUS使用任务感知的类别提示来调节不同数据集和标签空间的分割过程，使用动态卷积适应模块生成任务特定的输出响应，并使用拓扑信息损失鼓励解剖上一致的预测。在子宫超声数据集和T2加权子宫肌瘤MRI数据集上的实验表明，TPUS在两个保留测试集上分别取得了0.898和0.693的Dice分数，优于多个通用和通用分割基线。源代码可从此https URL访问。
英文摘要
Multi-structure segmentation of the uterus is important for computer-assisted screening, diagnosis, and treatment planning of uterine diseases, where ultrasound and MRI provide complementary clinical information. However, developing a unified model across these modalities is challenging due to their substantially different image appearances, anatomical contexts, spatial resolutions, and label spaces. Moreover, existing datasets often define different segmentation targets, making joint learning challenging and potentially leading to negative transfer across heterogeneous tasks. To this end, we propose a Topology-informed Prompt-conditioned Universal Segmentation (TPUS) framework for segmenting multiple uterine structures across ultrasound and MRI. TPUS introduces a graph-based multi-dataset backbone comprising modality-specific stems and a modality-shared graph-based encoder-decoder to support modality-sensitive input adaptation, structural feature reasoning, and joint representation learning across heterogeneous uterine segmentation tasks. In addition, TPUS uses task-aware class prompts to condition the segmentation process for different datasets and label spaces, a dynamic convolutional adaptation module to generate task-specific output responses, and a topology-informed loss to encourage anatomically consistent predictions. Experiments on a uterine ultrasound dataset and a T2-weighted uterine myoma MRI dataset demonstrate that TPUS achieves Dice scores of 0.898 and 0.693 on the two held-out test sets, respectively, outperforming several generic and universal segmentation baselines. Source code can be accessed at https://github.com/YonghengSun1997/TPUS.
Comments4 pages, 2 figures, 3 tables. Code: https://github.com/YonghengSun1997/TPUS
