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基于深度学习的方法,在非增强型CT扫描上对尿道和盆细分尿管和盆
Xin Jin1, Hai Zhong2, Yumeng Zhang3
1Institute of Marine Science and Technology, Shandong University, Qingdao, China.
Scientific reports
|August 30, 2024
概括
一种新的深度学习方法在非增强型CT扫描上准确地对上尿道 (UUT) 进行细分. 这种方法显示了提高UUT重建效率和减少CT泌尿图像等侵入性手术的潜力.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 精确的上泌尿道细分对于诊断和治疗规划至关重要.
- 非增强型计算机断层扫描 (NECT) 最好用于减少辐射暴露,但UUT可视化可能具有挑战性.
- 深度学习 (DL) 为自动图像细分提供了一个有希望的途径.
研究的目的:
- 开发和评估一种基于深度学习的方法,用于在NECT扫描上对UUT (骨和尿道) 的3D细分.
- 为了比较不同DL模型和UUT细分框架的性能.
- 评估开发方法的潜力,作为计算机断层扫描尿路图 (CTU) 的替代或补充.
主要方法:
- 使用三个DL模型 (基本UNet,UNet3+,ViT-UNet) 开发两个框架 (整体和部分).
- 培训和验证150个NECT扫描与正常的左UUT.
- 在29次NECT和CTU扫描中进行测试,经验丰富的放射科医生进行手动细分.
主要成果:
- 采用UNet基本模型的断面框架在测试组中实现了最高的平均精度 (85.5%) 和回忆率 (71.9%).
- 这种DL方法表明,与CTU相比,轴向UUT回忆率更高 (82.5%与69.1%,P <0.01).
- 在许多情况下,可视化与CTU相当或高于CTU,尽管观察到一个不可忽视的错误阳性率.
结论:
- 拟议的DL方法显示了在NECT扫描上实现自动化3D UUT细分的巨大潜力.
- 这种技术可以提高UUT重建的效率,并可能减少像CTU这样的侵入性检查的需要.
- 在UUT成像中,DL模型可以作为CTU的一个有价值的辅助.
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