摘要
目的 基于深度学习方法,运用自动分割模型分割MRI影像下女性尿控解剖元件结构,提高分割效率和准确率。方法 以来自陆军军医大学生物医学工程与影像医学系的49例女性盆底肌肉MRI图像(其中不同程度的盆腔脏器脱垂病例30例,正常人19例)作为数据集对模型进行训练和测试,按照8∶2的比例划分为训练集和测试集,最终选取17例正常人和22例盆腔脏器脱垂病例作为训练集,4例正常人和6例盆腔脏器脱垂病例作为测试集。将训练集放入UNet、UNet+++、Dense UNet以及UNet++模型分别训练,再将测试集放入网络中进行精度测试,选取精度最高的模型作为主干网络。结果 在UNet、UNet+++、Dense UNet以及UNet++模型的训练下,4个模型得到尿道压肌、尿道括约肌主体部、膀胱壁、膀胱腔、尿道黏膜下层5种结构的平均Dice相似性系数分别为61.82%、57.94%、57.63%和62.76%,交并比分别为49.74%、46.59%、46.07%和49.44%,准确率分别为61.74%、55.03%、59.23%和61.91%。结果显示UNet++的3个指标基本高于UNet、UNet+++、Dense UNet的指标,表明UNet++总体分割精度最优。结论 利用UNet、UNet+++、Dense UNet以及UNet++模型对女性盆底尿道压肌、尿道括约肌主体部、膀胱壁、膀胱腔、尿道黏膜下层5种结构进行了自动分割,其中UNet++总体分割精度最优。
Objective To construct an automatic segmentation model to segment female urine control anatomy on MRI images based on deep learning methods in order to improve the segmentation efficiency and accuracy.Methods A dataset comprising 49 female pelvic floor muscle MRI images[30 women with varying degrees of pelvic organ prolapse(POP)and 19 healthy individuals],obtained from Faculty of Biomedical Engineering and Medical Imaging in Army Medical University,was used for model training and testing.The dataset was split into a training set(17 normal cases and 22 POP cases)and a testing set(4 normal cases and 6 POP cases)in a ratio of 8∶2.The training set was used to train UNet,UNet+++,Dense UNet,and UNet++models separately,and then input into each network.The model achieving the highest testing accuracy was selected as the backbone network.Results Under the training of UNet,UNet+++,Dense UNet,and UNet++,the 4 models achieved average Dice similarity coefficients of 61.82%,57.94%,57.63%,and 62.76%,respectively,for the segmentation of 5 anatomical structures(compressor urethrae,urethra sphincter body,bladder wall,bladder cavity and urethra submucosa).The corresponding Intersection over Union(IoU)score was 49.74%,46.59%,46.07%,and 49.44%,while the accuracy rate was 61.74%,55.03%,59.23%,and 61.91%,respectively for the 4 models.Notably,UNet++consistently outperformed UNet,UNet+++,and Dense UNet across the 3 metrics,indicating that UNet++achieved the highest overall segmentation accuracy.Conclusion In UNet,UNet++,Dense UNet and UNet++for automatic segmentation of 5 female urine control anatomical elements,UNet++achieves the best overall segmentation accuracy.
作者
张子沁
吴毅
张小勤
徐洲
雷玲
王延洲
王艳
ZHANG Ziqin;WU Yi;ZHANG Xiaoqin;XU Zhou;LEI Ling;WANG Yanzhou;WANG Yan(School of Mathematical Sciences,Chongqing Normal University,Chongqing;Department of Digital Medicine,Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University(Third Military Medical University),Chongqing;Department of Gynecology,Anshun People’s Hospital,Anshun,Guizhou;Department of Obstetrics and Gynecology,First Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing,China)
出处
《陆军军医大学学报》
北大核心
2025年第14期1568-1576,共9页
Journal of Army Medical University
基金
国家自然科学基金(31971113)
重庆市自然科学基金(CSTB2023NSCQ-LZX0054)。
关键词
深度学习
图像分割
智能辅助诊断
核磁共振图像
deep learning
image segmentation
intelligent assisted diagnosis
nuclear magnetic resonance image
