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深度超像素生成和集群用于在MRI图像中对脑瘤进行弱监督的细分

Jay J Yoo^1,2,3,4, Khashayar Namdar^1,2,4, Farzad Khalvati^5,6,7,8,9,10

¹Institute of Medical Science, 1 King's College Circle, Toronto, M5S 1A8, Ontario, Canada.

BMC medical imaging

|December 18, 2024

概括

这项研究引入了针对脑瘤的新型弱监督细分管道,在没有手动注释的情况下实现了卓越的准确性. 该方法在MRI扫描中细分瘤方面优于CAM-S和SAM等现有模型.

科学领域:

医疗图像分析医学图像分析
机器学习机器学习
计算机视觉计算机视觉计算机视觉

背景情况:

医疗图像的手动细分是耗时和资源密集的.
自动化细分工具对于开发诊断辅助工具至关重要.
弱监督的学习为完全监督的方法提供了一个有希望的替代方案.

研究的目的:

开发一种机器学习管道,用于医学图像中细分瘤和异常.
为了使训练使用易于访问的二进制图像级分类标签,消除了对地面真相注释的需求.
以最少的手工输入有效地对感兴趣的地区进行细分.

主要方法:

同时训练了一个深度超像素生成模型和一个深度超像素聚类模型.
用二进制图像级标签来训练分类器,指导细分过程.
不够细分的种子被纳入作为损失条款,以定位感兴趣的地区.

主要成果:

该管道是从BraTS 2020和BraTS 2023数据集的2D切片上评估的.
提出的方法实现了0.745的平均子系数和20.8.95的平均HD95.
性能超过了最先进的基线,包括CAM-S (Dice: 0.646) 和SAM (Dice: 0.641).

结论:

深度超像素生成,聚类和指导定位的组合显著改善了弱监督的细分.
这种方法为脑瘤细分提供了一种高效有效的方法.
该管道展示了在医学成像中增强诊断工具的潜力.

关键词:

卷积神经网络是一种卷积神经网络.质瘤是一种质瘤.图像细分图像细分图像细分磁共振成像技术磁共振成像技术超级像素是一个超级像素.缺乏监督的学习学习.

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