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慢性病中使用3D卷积神经网络进行MRI自动细分的实用性

Kaiji Inoue¹, Yuki Hara¹, Keita Nagawa²

¹Department of Radiology, Saitama Medical University, 38 Morohongou, Moroyama-machi, Iruma-gun, Saitama, Japan.

Scientific reports

|October 13, 2023

概括

一种新的3D卷积神经网络 (CNN) 方法在使用MRI的慢性病 (CKD) 患者中自动细分脏. 该工具有助于测量脏总体积和用于CKD研究的图像分析.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
腎臟病學 (nephrology) 是一種醫學專業.

背景情况:

慢性病 (CKD) 影响全球数百万人,需要准确的监测工具.
精确测量总体积 (TKV) 对于评估慢性病的疾病进展和治疗疗效至关重要.
目前的细分方法可能是耗时和主观的,限制了大规模分析.

研究的目的:

开发和验证基于3D卷积神经网络 (CNN) 的自动细分方法.
评估CNN模型的性能,使用基于MRI的迪克森T1加权图像,对患有和没有功能障碍的患者进行评估.
评估开发的工具在CKD患者的脏总体积测量的实用性.

主要方法:

一个3D CNN模型被开发用于自动脏细分.
该模型使用基于MRI的迪克森T1加权的相位 (IP),相反相位 (OP) 和仅水 (WO) 图像.
数据集包括100名患有功能障碍 (RD) 的参与者和70名没有 (非RD) 的参与者,对非RD和组合组进行评估.

主要成果:

在使用IP图像的非RD组中,双边脏细分表现最好,达到了0.902 ± 0.034.3的Dice分数.
平均表面距离为1.46 ± 0.75毫米,体积差异为-27 ± 21毫升.
对于RD组合和非RD组以及单边细分,特别是OP图像中的右脏,观察到的性能略低.

结论:

开发的基于3D CNN的自动脏细分方法显示了临床应用的前景.
这种工具可以促进对慢性病患者体积和图像分析的大规模研究.
进一步验证和改进可能会提高不同患者群体和成像序列的性能.

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