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对心脏MRI细分的后期分布外检测

Tewodros Weldebirhan Arega¹, Stéphanie Bricq¹, Fabrice Meriaudeau²

¹ImViA Laboratory, Université de Bourgogne, Dijon, France.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|December 19, 2024

概括

这项研究引入了一种新方法来检测分布之外 (OOD) 的医疗图像,确保安全的AI使用. 这种方法有效地识别了各种OOD图像,优于现有的技术.

科学领域:

医疗图像分析医学图像分析
医疗保健中的人工智能
计算机视觉计算机视觉计算机视觉

背景情况:

由于扫描仪,协议或成像领域的变化,医疗图像细分模型面临着分布外 (OOD) 图像的挑战.
与OOD数据的不可预测模型行为需要强大的检测系统,以确保安全的临床应用.

研究的目的:

提出适用于任何预训练的医学图像细分模型的后期OOD检测方法.
为了提高AI驱动的医疗图像分析在现实世界的临床环境中的安全性和可靠性.

主要方法:

从细分模型编码器块中利用了多尺度的特征表示.
使用Mahalanobis距离来测量输入图像相似性与分布数据的相似性.
预训练的细分模型在心脏短轴电影MRI数据集上,并对13个OOD数据集 (近,轻,远) 进行评估.

主要成果:

拟议的Mahalanobis距离方法在各种OOD数据集中超过了最先进的特征空间和基于不确定性的OOD检测技术.
实现了近距离,轻度和远距离OOD图像的高检测精度,证明了多尺度编码器表示的价值.
一种基于子系数的方法显示出优异的对抗性OOD检测,并且与细分质量有很好的相关性.

结论:

开发的后期OOD检测方法,特别是使用Mahalanobis距离在多尺度特征上,为识别各种分布外的医疗图像提供了可靠的解决方案.
基于不确定性的方法在检测更不相似的 (轻微和远程) OOD 图像方面存在局限性.
未来的研究可能将Mahalanobis的距离和不确定性结合起来,以便更好地检测具有挑战性的OOD病例.

关键词:

心脏磁力共振成像 (MRI)在分销之外的检测检测分段化分段化分段化分段化不确定性不确定性

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