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在MRI上进行头部和部瘤细分,使用快速和资源高效的分期nnU网络

Elias Tappeiner¹, Christian Gapp¹, Martin Welk¹

¹UMIT Tirol - Private University for Health Sciences and Health Technology, Eduard-Wallnöfer-Zentrum 1, Hall in Tirol 6060, Austria.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings

|April 10, 2025

概括

这项研究引入了一种快速的两阶段人工智能方法,用于在MRI扫描中对头部和部瘤进行细分,从而改善适应性放射治疗计划. 该方法取得了具有竞争力的结果,提高了用于RT前和中期RT规划的瘤细分精度.

科学领域:

医疗成像医学成像
人工智能的人工智能
辐射疗法辐射疗法

背景情况:

与MRI相比,基于CT的传统放射治疗计划缺乏软组织对比度.
在头部和部MRI中手动瘤细分是耗时的,阻碍了适应性放射治疗.
HNTS-MRG挑战旨在为MR引导的应用程序推进自动瘤细分.

研究的目的:

开发一个高效的自动瘤细分方法,用于头部和部的MRI.
改进MR指导放射治疗中RT前和RT中期的规划.
为了解决手动细分在头癌治疗中的局限性.

主要方法:

实施了使用nnU-Net架构与剩余编码器的两阶段细分方法.
采用了一种新的两阶段战略,其中初始细分结果指导了改进阶段.
纳入的RT计划前信息作为中期RT细分精细化网络的输入.

主要成果:

在使用第一阶段nnU-Net.Net进行RT前细分时获得了80.97%的子系数.
通过使用第二阶段精炼网络,显著提高了中期RT细分性能.
对于前RT和中RT任务,已证明具有竞争力和增强的细分精度.

结论:

拟议的两阶段nnU-Net方法为头部和部瘤细分提供了快速和资源高效的解决方案.
该方法显示了通过改进的MRI细分来推进日常适应性放射治疗的巨大潜力.
代码和模型权重是公开的,以促进进一步的研究和应用.

关键词:

头部和部瘤头部和部瘤这就是为什么MRI是MRI.细分化细分化的细分化这是一个分阶段的nnU-Net.

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Magnetic Resonance Imaging

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