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快速-mamba过网络用于准确的肝细胞癌病变细分在腹部CTCT中

Long Xia^1,2, Hai-Yang Chen³, Ya-Wen Cao⁴

¹Department of Hepatobiliary Surgery, Hohhot First Hospital, Hohhot, Inner Mongolia, China.

NPJ digital medicine

|January 27, 2026

概括

在CT扫描中,Prompt-Mamba-AF增强了肝细胞癌 (HCC) 的细分. 该框架提高了小病变和多样化的成像的准确性,为临床工作流提供了有效的解决方案.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

在腹部CT中精确细分肝细胞癌 (HCC) 是至关重要的,但由于病变的变化和图像质量问题而具有挑战性.
现有的方法与形态异质性,小病变的低对比度和扫描器不一致性作斗争.

研究的目的:

在腹部CT图像中开发一个强大的框架,即Prompt-Mamba-AF,用于精确的HCC细分.
通过整合新的提示和建模策略来解决当前细分技术的局限性.

主要方法:

该Prompt-Mamba-AF框架利用解剖意识提示在肝脏内进行指导性特征提取.
它使用基于Mamba的状态空间建模来有效捕获远程体积依赖.
整合了结构感知过,以确保细分病变的拓一致性.

主要成果:

与最先进的CNN和变压器模型相比,Prompt-Mamba-AF在LiTS,3DIRCADb和CHAOS基准上表现优越.
该模型实现了领先的子相似性和边界精度,具有紧的参数足迹 (27.6M).
在检测小HCC结节和在不同成像领域的概括方面观察到显著的改善.

结论:

快速-Mamba-AF为HCC细分提供了一个高效和强大的解决方案,优于现有的方法.
该框架能够处理各种成像条件和小病变的能力使其适用于多中心临床应用.

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