深度学习用于对身体CT图像进行自动体积肠道细分
Junghoan Park1, Sungeun Park2, Han-Jae Chung3
1Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
European radiology
|May 2, 2025
概括
一个深度神经网络准确地划分了胃肠道,使便患者能够精确地估计大肠长度 (LBL). 这种人工智能工具为临床应用提供了一种非侵入性的方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 胃肠道的准确细分对于定量分析至关重要.
- 估计大肠长度 (LBL) 可以帮助诊断诸如便秘等疾病.
- 目前用于LBL测量的方法可能具有侵入性或缺乏精度.
研究的目的:
- 开发一个深度神经网络 (DNN) 用于CT图像上的自动化肠道细分.
- 评估DNN对整个胃肠道及其隔间的细分的准确性.
- 评估DNN在估计LBL及其与便秘的相关性中的实用性.
主要方法:
- 利用3D nnU-Net模型在对比度增强和非增强CT扫描中进行训练.
- 将肠道细分为食道,胃,小肠和大肠.
- 使用子相似系数 (DSC) 评估细分精度,并比较了便秘和非便秘组之间的LBL.
主要成果:
- 该DNN实现了高分段精度的GI通道 (平均DSC0.985) 和其四个隔间 (DSC>0.95),优于现有的方法.
- 外部测试证实了该车型的强大性能.
- 高度校正的LBL在便秘患者中明显长,与没有便秘患者相比.
结论:
- 开发的3D nnU-Net模型从CT数据中提供了肠道及其主要组成部分的准确细分.
- 这种人工智能驱动的方法使得LBL的非侵入性估计成为可能,显示出诊断和管理便秘的临床应用的潜力.
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