在T2MRI中通过MR-to-CT合成对骨盆结构的细分
Yan Zhuang1, Tejas Sudharshan Mathai1, Pritam Mukherjee1
1Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, 20892, MD, USA.
概括
这项研究引入了一种无注释的方法,通过将其转化为CT,在MRI扫描中对骨盆结构进行细分. 这种方法实现了8个骨盆结构的超过65%的Dice测量,有助于临床应用.
科学领域:
- 医疗成像医学成像
- 计算解剖学的计算解剖学
- 放射学 放射学是一门学科.
背景情况:
- 在MRI中骨盆结构的细分对于临床应用至关重要,例如对肉症的评估和身体成分分析.
- 现有的自动化细分方法主要集中在CT上,对MRI的选择有限.
- 盆腔MRI的手动细分是劳动密集和耗时的.
研究的目的:
- 开发一种轻量级,无注释的管道,用于在T2MRI卷中对多个骨盆结构进行细分.
- 利用现有的基于CT的对MRI数据的细分工具.
- 为临床应用提供手动细分的替代方案.
主要方法:
- 提出了一个新的管道,以合成地将T2盆腔MRI体积转化为CT.
- 在翻译的CT卷上使用了现有的仅用于CT的细分工具TotalSegmentator.
- 在CT上生成的细分面具被映射回原来的MRI卷.
主要成果:
- 拟议的管道实现了Dice相似系数 (DSC) 测量≥65%的8个盆腔结构在T2MRI.
- 该方法在公开的TCGA-UCEC数据集和内部数据集上都表现出有效性.
- 管道成功地分割了多个骨盆器官和结构,没有手动注释.
结论:
- 开发的管道为MRI中的多结构骨盆细分提供了一个可行的,无注释的解决方案.
- 这种方法弥合了先进的CT细分技术和基于MRI的应用之间的差距.
- 该方法有可能扩展到其他MRI序列和解剖区域.
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