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一种计算机辅助的确定方法,用于MRI图像上早期子宫内膜癌的肌体透深度

Liu Xiong¹, Chunxia Chen², Yongping Lin³

¹School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No. 600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China.

Biomedical engineering online

|November 1, 2023

概括

使用多阶段深度学习的新计算机辅助诊断 (CAD) 方法通过在MRI扫描上测量肌肉透深度来准确地分类早期的子宫内膜癌 (EC). 这种人工智能方法与放射科医生的性能相匹配,为早期EC检测提供了一个直观的工具.

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

早期子宫内膜癌 (EC) 的分类依赖于精确评估子宫内膜透 (MI) 深度.
磁共振成像 (MRI) 对于评估EC至关重要,但手动解释可能具有挑战性.
深度学习 (DL) 提供了自动化和改善医学成像诊断准确性的潜力.

研究的目的:

开发和验证计算机辅助诊断 (CAD) 系统,使用多阶段深度学习模型来分类早期子宫内膜癌.
为了准确地确定肌肉膜透 (MI) 的深度,用T2权重的MRI图像 (T2WI) 来进行EC分期.
将开发的CAD方法与人类放射科医生在早期EC分类中的性能进行比较.

主要方法:

对154名早期EC患者 (FIGO阶段IA和IB) 的回顾性分析,病理确诊.
实现多阶段DL模型,包括基于SSD的检测模型和基于Attention U-net的图像分析细分模型.
利用圆配合算法生成子宫腔线 (UCL) 进行精确的MI深度测量和随后的EC分类.

主要成果:

该检测模型在子宫 (98.70%) 和瘤 (87.93%) 中实现了高精度.
最佳的MRI切片选择显示了97.83%的准确性.
细分模型显示出强大的表现 (平均IOU:0.738,平均PA:0.867,平均DSC:0.845).
最终的CAD方法获得了86.9%的准确度,81.8%的灵敏度和91.7%的早期EC分类特异性,与放射科医生相比.

结论:

开发的CAD方法为基于T2WI的MI深度的早期EC分类提供了一个端到端的解决方案.
基于深度学习的CAD系统表现出与专家放射科医生相当的性能.
这种人工智能方法为早期子宫内膜癌的现有DL-based诊断工具提供了更直观和可解释的替代方案.

关键词:

计算机辅助诊断是一种计算机辅助的诊断.深度学习是一种深度学习.磁共振成像技术磁共振成像技术子宫腔线的子宫腔线.

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