一个深度学习模型用于在狗的非对比计算机断层扫描上自动检测结石
Yewon Ji1, Gyeongyeon Hwang2, Sang Jun Lee2
1Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea.
Frontiers in veterinary science
|October 6, 2023
概括
这项研究引入了一种深度学习模型,用于使用CT扫描自动检测狗的结石 (结石). 该模型有助于早期诊断,改善患者监测和诊断这种常见的狗尿路疾病的预后.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 腎病是狗中常見的尿路疾病,可能導致嚴重的并發症,如阻塞和腎上腺體損失.
- 早期诊断结石对于有效的患者管理和改善预后至关重要.
- 目前通过计算机断层扫描 (CT) 检测和监测结石的方法对兽医来说是耗时的.
研究的目的:
- 开发一种新的深度学习模型,用于使用前对比CT图像在狗中自动检测结石.
- 评估各种深度学习架构在犬类CT扫描中对脏和块细分的性能.
主要方法:
- 使用34,655个横向图像切片从76只患有结石的狗身上开发了一个深度学习模型.
- 该模型包含了注意力U-Net (AttUNet) 用于初始脏检测.
- 五个不同的模型 (AttUNet,UTNet,TransUNet,SwinUNet,RBCANet) 被评估用于检测calculi,使用子相似系数 (DSC) 评估性能.
主要成果:
- 与以前的模型相比,所有开发的模型都显示了用于自动脏细分的改进的子相似系数 (DSC).
- 在结石检测方面,RBCANet和SwinUNet获得了0.74的最高DSC.
- 深度学习方法显示了自动细分和检测犬结石的巨大潜力.
结论:
- 开发的深度学习模型为自动检测狗的结石提供了一个有前途的工具.
- 这项技术可以帮助兽医更快,更准确地进行诊断,从而改善患者的治疗结果.
- 该模型的进一步研究和应用可以简化犬病的诊断过程.
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