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一个基于深度学习的计算机断层扫描阅读系统,用于诊断与囊性空气空间相关的肺癌

Zeyang Hu¹, Xia Zhang², Jinqiu Yang²

¹The Third People's Hospital Health Care Group Of Cixi, Ningbo, China.

Scientific reports

|July 2, 2025

概括

一个新的深度学习模型,LungSSFNet,显示了使用CT扫描诊断囊性空气空间 (LCCA) 的肺癌的前景. 它在细分方面实现了高精度,有助于早期检测和临床决策.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

与囊性空气空间相关的肺癌 (LCCA) 需要准确的诊断工具.
计算机断层扫描 (CT) 成像对于肺结节的检测至关重要.
深度学习有可能提高医学成像诊断的准确性.

研究的目的:

开发和评估一个深度学习模型,LungSSFNet,用于LCCA的辅助诊断.
评估LungSSFNet在LCCA和肺泡的图像识别和细分方面的表现.
将LungSSFNet与LCCA诊断的现有深度学习模型进行比较.

主要方法:

追溯分析342个CT系列 (272个LCCA,70个肺泡).
基于nnUnet框架的LungSSFNet的开发.
数据集分为培训 (245),验证 (62) 和测试 (35) 集.
对UNet,M2Snet,TANet,MADGNet和nnUnet进行比较性绩效评估.

主要成果:

肺SSFNet实现了高细分性能:LCCA的IOU为81.05%和DICE为75.15%;肺泡的IOU为93.03%和DICE为92.04%.
与其他几种深度学习模型相比,该模型表现出了优异的性能.
精度达到96.77%,精度达到100%,LCCA诊断的灵敏度为96.15%.

结论:

肺SSFNet显著提高了早期LCCA的辅助诊断.
该模型显示了作为肺癌诊断中临床决策的有价值工具的潜力.
开发的LungSSFNet代码是公开可用的,用于研究.

关键词:

计算机断层扫描 (CT) 扫描卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.美国LCCA LCCA 肺部结节肺部结节

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