在MRI上对女性盆地肌肉进行自动细分,以评估盆地功能
Xiaoqin Zhang1, Yongjia Xiang2,3, Jie Yao1
1Department of Digital Medicine, College of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China.
Quantitative imaging in medicine and surgery
|July 17, 2023
概括
一个改进的DenseUnet模型在MRI扫描中自动细分盆底肌肉,为评估盆底功能障碍的手动方法提供了更快,更准确的替代方案.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 盆腔健康 盆腔健康
背景情况:
- 骨盆器官脱落 (POP) 是女性常见的骨盆底功能障碍.
- 盆地肌肉体积,特别是升高肌肉 (LAM),对于评估盆地功能至关重要.
- 用于测量肌肉体积的手动细分是耗时且临床上不切实际的.
研究的目的:
- 开发一个高效的自动细分模型,用于盆地肌肉使用MRI.
- 为了能够快速准确地计算肌肉体积,用于POP评估.
- 为评估骨盆地功能提供一个有价值的工具.
主要方法:
- 追溯分析49个女性盆地MRI系列 (21个正常,28个有POP).
- 手动细分LAM,内部阻塞肌 (IOM) 和外部关节 (EAS).
- 与标准模型 (DenseUnet,ResUnet,Unet++,Unet) 相比,开发和评估一个改进的DenseUnet模型,用于自动肌肉细分.
主要成果:
- 改进的DenseUnet实现了高细分性能,平均子相似系数 (DSC) 为0.758 (LAM),0.716 (IOM) 和0.810 (EAS).
- 与手动细分相比,细分精度没有显著差异 (P > 0.05).
- 自动细分平均每案10.18秒,与手动细分 (45分钟) 相比,大大减少了时间.
结论:
- 改进的DenseUnet提供了一种快速,高效和精确的方法,用于通过MRI对盆底肌肉进行细分.
- 这种自动化方法大大缩短了评估时间,帮助临床医生评估盆地功能.
- 该模型为肌肉体积计算和POP评估提供了可靠的参考.
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