基于使用修改后的U-Net进行非对比增强计算机断层扫描图像的脏,尿道和尿囊细分
Dong-Hyun Jang1, Juncheol Lee2, Young-Jin Jeon3
1Department of Public Healthcare Service, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Scientific reports
|July 3, 2024
概括
这项研究开发了一种修改后的U-Net模型,用于在非对比CT扫描中对尿道系统进行细分. 这种人工智能驱动的方法有助于诊断尿路疾病,如结石和瘤.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 尿道系统的准确细分对于在非对比计算断层扫描 (CT) 上诊断疾病至关重要.
- 现有的细分方法可能需要改进,以便在尿路疾病诊断中进行临床应用.
研究的目的:
- 开发和评估一种修改后的U-Net模型,用于在非对比CT扫描上对分泌系统 (脏,尿道,尿囊) 进行细分.
- 建立基于机器学习的尿道系统病变检测的基础.
主要方法:
- 利用诊断出尿路结石的患者的非对比腹腔皮层CT扫描.
- 应用感兴趣的区域提取,然后使用修改的U-Net架构进行尿路系统细分.
- 使用五倍交叉验证和单独的测试数据集评估模型的稳定性和性能.
主要成果:
- 在整个泌尿系统细分的五倍交叉验证中获得了0.8673的平均Dice系数.
- 具体子系数为脏的0.9651,尿道的0.7172和尿道的0.9196.
- 在测试数据集中表现最好的模型在整个系统中平均产生0.8623的Dice系数.
结论:
- 修改后的U-Net模型有效地在非对比CT中对尿道系统进行细分.
- 这种细分技术为基于机器学习的尿路病理检测提供了基础.
- 该模型有望改善结石和膀瘤等疾病的诊断.
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