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MCF-SMSIS:多任务与补充功能,用于立体声匹配和手术仪器细分

Renkai Wu¹, Changyu He², Pengchen Liang¹

¹School of Microelectronics, Shanghai University, Shanghai, China; Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Computers in biology and medicine

|July 25, 2024

概括

这项研究引入了一种多任务框架 (MCF-SMSIS),它共同执行立体匹配和手术仪器细分,改善机器人手术自动化. 综合方法提高了分析腹腔镜手术场景的准确性和效率.

科学领域:

机器人和自动化机器人与自动化
计算机视觉计算机视觉
医疗成像医学成像

背景情况:

立体声匹配和仪器细分对于机器人手术自动化至关重要,但通常是单独研究的.
在腹腔镜外科手术场景中,这些任务之间的相互依赖在很大程度上被忽视了.

研究的目的:

提出一个新的多任务学习框架 (MCF-SMSIS),集成立体匹配和手术仪器细分.
为了利用仪器细分功能来提高立体声匹配的准确性.
在立体匹配中引入新的评估指标 (MINPD,MAXPD) 进行域迁移评估.

主要方法:

开发了一个多任务框架 (MCF-SMSIS),集成立体声匹配和仪器细分.
嵌入的仪器细分功能进入了立体相匹配的抛物线匹配块.
在SCARED,SERV-CT和AutoLaparo数据集上进行了实验.
拟议的MINPD和MAXPD指标用于评估立体匹配中的域迁移.

主要成果:

拟议的方法显著提高了立体相匹配的准确性,在手术仪器区域分别减少了EPE,>3px和RMSE深度9.5%,12.7%和6.51%.
在仪器细分方面获得0.9233的子相似系数 (DSC).
每张图像集的有效推断时间为0.14秒.
在多个数据集中验证有效性.

结论:

综合多任务方法 (MCF-SMSIS) 在腹腔镜手术中有效地增强了立体匹配和仪器细分.
任务之间的互补特性导致机器人手术自动化中的性能和效率提高.
拟议的框架和指标为分析外科视频数据提供了进步.

关键词:

评估功能是一个评估功能.工具细分化工具细分化多任务互补学习多任务互补学习立体声匹配配对应手术视力手术视力

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