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一个新的数据集和多功能的多任务手术工作流分析框架,用于胸腔镜中部膜整形

Meng Lan¹, Weixin Si², Xinjian Yan³

¹Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China.

Medical image analysis

|July 30, 2025

概括

这项研究介绍了TMVP-SurgVideo,这是第一个用于胸腔镜心肌膜整形工作流程分析的数据集,以及SurgFormer,这是一个用于手术阶段和仪器识别和预测的新型AI框架. 这推动了人工智能辅助的心脏手术的发展.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

手术工作流分析 (SWA) 对人工智能辅助手术至关重要,但对复杂的心脏手术研究不足.
现有的SWA方法主要局限于腹腔镜手术,使胸腔镜心脏手术在很大程度上未被探索.

研究的目的:

介绍第一个视频数据集,TMVP-SurgVideo,用于胸腔镜心脏中枢膜整形术 (TMVP) 工作流分析.
提出SurgFormer,一个新的AI框架,用于同时识别和预测TMVP中的手术阶段和仪器.
为了使线下和线上推断能够进行全面的SWA.

主要方法:

开发了TMVP-SurgVideo数据集,包含57个视频和超过429K个注释.
提出了SurgFormer,这是一个基于查询的Transformer框架,具有特定任务的嵌入和信息交互模块.
在SurgFormer中内置的框架内任务级别和框架间时间相关性学习.
启用了动态内存库,可以在线和离线推断,而无需修改模型.

主要成果:

在TMVP-SurgVideo数据集上,SurgFormer证明了它的有效性.
在现有的Cholec80数据集上评估了SurgFormer,显示了它的多功能性.
拟议的框架成功地执行了手术阶段和仪器的识别和预测.

结论:

TMVP-SurgVideo是第一个数据集,使SWA研究能够在胸腔镜心脏中枢膜造形术中进行.
SurgFormer为TMVP中的SWA提供了一个全面有效的AI解决方案,超越了现有方法的局限性.
开发的数据集和框架推动了复杂的心脏手术的人工智能辅助智能手术.

关键词:

长形式视频数据集长形式视频数据集多任务学习是多任务学习.外科手术工作流程分析

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