发表机构
KAIST; Korea University Guro Hospital; Samsung Medical Center（韩国科学技术院; 高丽大学九老医院; 三星医疗中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
XFlow提出一种结合框定位与多点细化的工作流模型，用于胸部X光片指令引导病变分割，通过多级感知过程提升分割质量，超越现有模型ROSALIA。
AI 中文摘要
现有的医学领域文本引导分割模型仅覆盖胸部X光片（CXR）中狭窄的解剖结构和病变集合，且大多数模型假设查询目标总是存在于图像中。指令引导病变分割（ILS）通过从简单用户指令中分割多种病变类型，同时识别查询病变不存在的情况，来克服这些限制，ROSALIA被提出作为该任务的第一个模型。然而，ROSALIA产生的掩膜质量仍然有限，常常带有分散的噪声。此外，ROSALIA以单次方式预测掩膜，这与放射科医生在实践中感知和描绘病变的方式根本不同。放射科医生首先检查整个胸腔，然后定位异常的大致区域，最后细化病变轮廓。受这种从粗到细、多级感知过程的启发，我们提出了XFlow，一种用于ILS的工作流模型，它结合了基于框的定位和多轮点细化。XFlow检测肺部，决定查询的发现是否存在于每个肺中，并用病变框提示微调后的SAM以生成初始掩膜。然后，它通过点提示纠正该掩膜，直到其边界跟随病变，使每个中间决策都可见。我们的实验表明，XFlow在内部和外部评估上都达到了最佳分割质量。值得注意的是，即使两者在相同的病变标注上训练，它在分割质量上也超过了ROSALIA。代码和模型权重将公开提供。
英文摘要
Existing text-guided segmentation models in the medical domain cover only a narrow set of anatomical structures and lesions in chest X-rays (CXRs), and most of them assume that the queried target is always present in the image. Instruction-guided lesion segmentation (ILS) was introduced to overcome these limitations by segmenting diverse lesion types from simple user instructions while also recognizing when the queried lesion is absent, and ROSALIA was proposed as the first model for this task. However, the masks produced by ROSALIA remain of limited quality, often carrying scattered noise. Moreover, ROSALIA predicts the mask in a single shot, which differs fundamentally from how radiologists perceive and delineate lesions in practice. A radiologist first surveys the entire thorax, then localizes the approximate region of abnormality, and only then refines the lesion contour. Motivated by this coarse-to-fine, multi-level perception process, we present XFlow, a workflow model for ILS that combines box-based localization with multi-turn point refinement. XFlow detects the lungs, decides whether the queried finding is present in each of them, and prompts a fine-tuned SAM with the lesion box for an initial mask. It then corrects that mask through point prompts until its boundary follows the lesion, leaving every intermediate decision visible. Our experiments show that XFlow achieves the best segmentation quality on both internal and external evaluation. Notably, it surpasses ROSALIA in segmentation quality even when the two are trained on the same lesion annotations. Code and model weights will be made publicly available.
