Zhang Xiaoqin, Xiang Yongjia, Yao Jie, Hu Xin, Wang Yangyun, Liu Liping, Wang Yan, Wu Yi
Department of Digital Medicine, College of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China.
School of Mathematical Sciences, Chongqing Normal University, Chongqing, China.
Quant Imaging Med Surg. 2023 Jul 1;13(7):4181-4195. doi: 10.21037/qims-22-1198. Epub 2023 May 24.
Pelvic organ prolapse (POP) is a pelvic floor dysfunction disease which affects females. The volume of pelvic floor muscle, especially the levator ani muscle (LAM), is an important indicator of pelvic floor function. However, muscle volume measurements depend on manual segmentation, which is clinically time-consuming. In this work, we present an efficient automatic segmentation model of pelvic floor muscles with magnetic resonance imaging (MRI) based on DenseUnet, to achieve muscle volume calculation and provide a reference for the assessment of pelvic floor function.
A total of 49 female pelvic floor magnetic resonance (MR) series were retrospectively enrolled from the First Affiliated Hospital of Army Military Medical University between 2013 and 2021, including 21 normal participants and 28 patients with stage 1-4 POP. The LAM, internal obturator muscle (IOM), and external anal sphincter (EAS) were manually segmented. An improved DenseUnet was proposed for automatic segmentation of these 3 muscles. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetrical surface distance (ASSD) were used to evaluate segmentation results. The segmentation performance of the improved DenseUnet was compared with those of standard DenseUnet, ResUnet, Unet++, and Unet.
The improved DenseUnet showed a good performance. The average DSC and standard deviation of the LAM, IOM, and EAS was 0.758±0.151, 0.716±0.173, and 0.810±0.147, respectively. The average HD was 22.41, 19.00, and 36.01 mm, respectively; and the average ASSD was 3.66, 3.80, and 5.23 mm, respectively. The average DSC and standard deviation of the normal group and POP group was 0.779±0.166 and 0.757±0.154, respectively. There was no significant difference between the muscle volume of the improved DenseUnet and manual segmentation (all P values >0.05). The average total segmentation time for 1 case was 10.18 s on our setup, which is much lower than the manual segmentation time of 45 minutes.
The improved DenseUnet segments the pelvic floor muscles in MRI quickly and efficiently, with good precision and faster speed than those of manual segmentation. This can assist doctors in quickly segmenting pelvic floor muscles, calculating muscle volume, and further evaluating pelvic floor function.
盆腔器官脱垂（POP）是一种影响女性的盆底功能障碍性疾病。盆底肌肉的体积，尤其是肛提肌（LAM），是盆底功能的一个重要指标。然而，肌肉体积测量依赖于手动分割，这在临床上很耗时。在本研究中，我们基于密集型U-Net提出了一种利用磁共振成像（MRI）对盆底肌肉进行高效自动分割的模型，以实现肌肉体积计算，并为盆底功能评估提供参考。
回顾性纳入2013年至2021年间陆军军医大学第一附属医院的49例女性盆底磁共振（MR）序列，包括21名正常参与者和28例1-4期POP患者。对LAM、闭孔内肌（IOM）和肛门外括约肌（EAS）进行手动分割。提出一种改进的密集型U-Net用于这3种肌肉的自动分割。采用骰子相似系数（DSC）、豪斯多夫距离（HD）和平均对称表面距离（ASSD）来评估分割结果。将改进的密集型U-Net的分割性能与标准密集型U-Net、ResU-Net、U-Net++和U-Net的分割性能进行比较。
改进的密集型U-Net表现良好。LAM、IOM和EAS的平均DSC及标准差分别为0.758±0.151、0.716±0.173和0.810±0.147。平均HD分别为22.41、19.00和36.01mm；平均ASSD分别为3.66、3.80和5.23mm。正常组和POP组的平均DSC及标准差分别为0.779±0.166和0.757±0.154。改进的密集型U-Net分割的肌肉体积与手动分割结果之间无显著差异（所有P值>0.05）。在我们的设备上，1例的平均总分割时间为10.18秒，远低于45分钟的手动分割时间。
改进的密集型U-Net能快速、高效地对MRI中的盆底肌肉进行分割，精度良好，速度比手动分割更快。这有助于医生快速分割盆底肌肉、计算肌肉体积，并进一步评估盆底功能。
