发表机构
Department of Artificial Intelligence, Sungkyunkwan University, Republic of Korea; Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea; Department of MetaBioHealth, Sungkyunkwan University, Republic of Korea（人工智能系，成均馆大学，大韩民国; 放射肿瘤科，三星医疗中心，成均馆大学医学院，大韩民国; 元生物健康系，成均馆大学，大韩民国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对质子治疗计划中NCCT图像病变分割因对比度低而困难的问题，提出ViPSAM框架，基于SAM模型，利用视觉提示编码器和视觉引导交叉注意力模块整合跨模态信息，实验证明其在肝脏病变分割上优于其他方法，能实现更准确稳健的分割。
AI 中文摘要
在质子治疗计划中，呼吸门控非增强CT（NCCT）常用于病变分割，但由于病变与背景对比度低，准确勾勒仍具有挑战性。尽管基于学习的方法表现出强大性能，但它们在非增强图像分割中往往存在困难。受临床实践启发，我们提出了ViPSAM，一个利用互补跨模态信息的视觉提示框架。它基于Segment Anything模型（SAM）构建，引入视觉提示编码器从增强图像中提取引导特征，以及视觉引导交叉注意力模块整合非增强和增强特征，增强低对比度区域中与病变相关的表示。掩码解码器也以参数高效的方式进行了调整以有效利用视觉提示。我们在质子治疗获取的NCCT肝脏病变分割上评估了该方法。实验结果表明ViPSAM优于基于U-Net和SAM的代表性方法，表明跨模态视觉提示能在非增强图像中实现更稳健和准确的分割。
英文摘要
In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast. Although learning-based methods have shown strong performance, they often struggle with non-contrast image segmentation. Inspired by clinical practice, where contrast-enhanced MRI is referenced to delineate lesions on NCCT, we propose ViPSAM, a visual prompting framework that leverages complementary cross-modality information. Built upon the Segment Anything Model (SAM), ViPSAM introduces a visual prompt encoder to extract guidance features from contrast-enhanced images and a visual-guided cross-attention module to integrate non-contrast and contrast-enhanced features, thereby enhancing lesion-relevant representations in low-contrast regions. The mask decoder is further adapted in a parameter-efficient manner to utilize visual prompts effectively. We evaluate the proposed method on liver lesion segmentation using NCCT acquired for proton therapy. Experimental results demonstrate that ViPSAM outperforms representative U-Net- and SAM-based methods, indicating that cross-modality visual prompting enables more robust and accurate segmentation in non-contrast images.
CommentsAccepted at MICCAI 2026
