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相关实验视频

Updated: May 20, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

专家指导和部分标记的数据协作,用于多机关细分.

Li Li¹, Jianyi Liu¹, Hanguang Xiao²

¹National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, 710049, Shaanxi, China.

Neural networks : the official journal of the International Neural Network Society

|March 25, 2025

概括

本研究介绍了EGPD-Seg,这是一种使用计算机断层扫描 (CT) 扫描进行腹部多器官细分的新框架. 它有效地将有限的单个器官标签与专家指导相结合,以提高细分精度,减少数据注释负担.

关键词:

腹部器官腹部器官 CT图像细分的部分化CT图像细分专家指导专家指导多机关细分化多机关细分化

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Last Updated: May 20, 2025

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Published on: July 5, 2024

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科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

在CT扫描中腹部多器官细分对于临床应用至关重要.
目前的方法需要广泛的单一机构数据集或集中式的多机构数据,增加标签和收集负担.
单器官标签比多器官标签更容易获得和更具成本效益.

研究的目的:

开发一个高效的协作框架,用于使用部分标记数据进行多器官细分.
减少在医疗图像细分中对大型,完全注释的数据集的依赖.
提出一种有效利用单器官和多器官标签的方法.

主要方法:

提出了一个由专家指导和部分标记的数据协作框架 (EGPD-Seg).
引入了奖励-惩罚损失功能,以专注于单个器官目标并减轻未标记器官的影响.
开发了一个专家指导的模块,用于学习先前的知识,以从单个器官标记数据中对未标记的器官进行细分.

主要成果:

在部分标签设置下,EGPD-Seg证明了有效的多器官细分性能.
该框架在五个不同的腹部多器官细分数据集上得到了验证,包括内部和外部验证.
拟议的模块相互作用,以提高细分精度,使用有限的标签.

结论:

EGPD-Seg为腹部多器官细分提供了有效的解决方案,并减少了注释要求.
单个和多器官标签之间的协作机制,在专家投入的指导下,显著提高了细分性能.
这种方法解决了医疗图像细分中的数据挑战,使其更适合临床使用.