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Sli2Vol+:基于对象估计指导通信流网络的3D医疗图像细分.

Delin An¹, Pengfei Gu², Milan Sonka³

¹University of Notre Dame.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision

|September 26, 2025

概括

Sli2Vol+通过使用一种新的自我监督框架,减少了对3D医疗图像细分的注释需求. 这种方法有效地传播一个单一的注释片段,用于对解剖结构进行细分,提高跨不同数据集的概括性.

科学领域:

医学成像医学成像
人工智能的人工智能是人工智能.
计算机视觉计算机视觉计算机视觉

背景情况:

深度学习 (DL) 在医学图像细分方面表现出色,但需要大量的注释数据,这对于3D卷来说是昂贵和难以获得的.
现有的面具传播DL方法减少了注释负担,但存在错误积累,并与切片之间的不连续性作斗争.

研究的目的:

介绍Sli2Vol+,一个新的自主监督框架 (SSF) 用于3D医疗图像细分,每卷只使用单个注释片.
解决以前方法的局限性,特别是错误积累和处理不连续性的问题.

主要方法:

Sli2Vol+通过在训练卷中传播注释的2D切片来生成伪标签 (PL).
开发了一种新的对象估计指导对应流网络,用于自主监督学习切片和PL之间的对应.
在测试阶段,这些学习的对应函数被用来传播一个单一的注释切片用于细分.

主要成果:

该方法在各种医疗图像细分任务和数据集中表现出有效性.
Sli2Vol+在不同器官,模式和成像模式中显示出更好的概括性.
这种方法成功地对解剖结构进行细分,并大大减少了注释工作.

结论:

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Sli2Vol+为3D医疗图像细分提供了有效的解决方案,要求注释最小.
拟议的SSF克服了先前面具传播技术的局限性,提高了可靠性和准确性.
这种方法有可能在医学图像分析中得到更广泛的应用,从而促进高效的细分工作流程.