文本可提示传播：面向医学图像序列引用分割
Text-Promptable Propagation for Referring Medical Image Sequence Segmentation
AI总结：
针对引用医学图像序列分割任务中现有模型难以跨序列跟踪目标及缺乏文本交互引导的问题，提出Text-Promptable Propagation模型，通过跨模态交互与Transformer传播实现目标识别与跟踪，构建大规模基准并取得优异性能。
摘要：
引用医学图像序列分割（Ref-MISS）是一项新颖且具挑战性的任务，旨在基于自然语言描述分割医学图像序列（如内窥镜、超声、CT和MRI）中的解剖结构。该任务具有重要临床潜力，为医学影像解读提供了用户友好的进步。现有2D和3D分割模型难以明确跟踪医学图像序列中的目标对象，且缺乏对交互式文本驱动引导的支持。为解决这些局限性，我们提出文本可提示传播（TPP）模型，用于引用医学图像序列分割。TPP捕捉序列图像间的内在关系及相关文本描述，通过跨模态引用交互识别目标对象，并利用基于Transformer的三重传播在序列中持续跟踪目标。我们构建了大规模基准Ref-MISS-Bench，覆盖4种成像模态和20种器官与病变。实验结果表明，TPP在医学分割和引用视频对象分割任务中持续优于现有方法。
英文摘要：
Referring Medical Image Sequence Segmentation (Ref-MISS) is a novel and challenging task that aims to segment anatomical structures in medical image sequences (\emph{e.g.} endoscopy, ultrasound, CT, and MRI) based on natural language descriptions. This task holds significant clinical potential and offers a user-friendly advancement in medical imaging interpretation. Existing 2D and 3D segmentation models struggle to explicitly track objects of interest across medical image sequences, and lack support for nteractive, text-driven guidance. To address these limitations, we propose Text-Promptable Propagation (TPP), a model designed for referring medical image sequence segmentation. TPP captures the intrinsic relationships among sequential images along with their associated textual descriptions. Specifically, it enables the recognition of referred objects through cross-modal referring interaction, and maintains continuous tracking across the sequence via Transformer-based triple propagation, using text embeddings as queries. To support this task, we curate a large-scale benchmark, Ref-MISS-Bench, which covers 4 imaging modalities and 20 different organs and lesions. Experimental results on this benchmark demonstrate that TPP consistently outperforms state-of-the-art methods in both medical segmentation and referring video object segmentation.
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