Deep learning enables automatic quantitative assessment of puborectalis muscle and urogenital hiatus in plane of minimal hiatal dimensions
Deep learning enables automatic quantitative assessment of puborectalis muscle and urogenital hiatus in plane of minimal hiatal dimensions
复制标题
DOI：
10.1002/uog.20181
复制
发表时间：
2019-08-01
影响因子：
7.1
通讯作者：
van Stralen, M.
中科院分区：
文献类型：
--
作者：
van den Noort, F.;van der Vaart, C. H.;van Stralen, M.
目的在经会阴超声（TPUS）图像上，在最小裂孔平面内自动测量尿生殖裂孔（UH）的长度、宽度、面积以及耻骨直肠肌（PRM）的长度和平均回声强度（MEP），通过使用深度学习自动分割UH和PRM。方法在1318个三维和四维（3D/4D）对253例孕12周和36周未产妇的TPUS容积数据集，分别在PRM静息、最大收缩和最大Valsalva动作时，手工获得最小食管裂孔平面二维图像，并对UH和PRM进行人工分割。总共有713张图像用于训练卷积神经网络（CNN），以在最小裂孔尺寸的平面中自动分割UH和PRM。在数据集的其余部分（测试集1（TS 1）; 601张图像，其中4张被排除在外）中，通过比较自动和手动分割来评估CNN的性能。CNN的性能还在来自40只妊娠12周的初产妇的独立数据集（测试集2（TS 2）;两张图像已被排除）的117张图像上进行了测试，这些图像由不同的观察者手动采集和分割。视觉上评估自动分割的成功。基于CNN分割，测量以下临床相关参数：UH的长度、宽度和面积，PRM和MEP的长度。测量手动和CNN分割之间的重叠（Dice相似性指数（DSI））和表面距离（平均绝对距离（MAD）和Hausdorff距离（HDD））以研究它们的相似性。对于测量的临床相关参数，人工和CNN结果之间的组内相关系数（ICC）determined.Results全自动CNN分割成功的图像在TS 1和TS 2分别为99.0%和93.2%。DSI、MAD和HDD在两个测试集中显示了手动和CNN分割之间的良好重叠和距离。这反映在TS 1和TS 2中长度的相应ICC值中（0.96和0.95），宽度（0.77和0.87）和面积UH的（0.96和0.91），PRM的长度（0.87和0.73）和MEP（0.95和0.97），结论深度学习可用于自动可靠地分割2D超声上的PRM和UH最小食管裂孔平面内的未产骨盆底图像。这些分割可以用于可靠地测量UH尺寸以及PRM长度和MEP。(c)2018作者由John Wiley & Sons Ltd代表国际妇产科超声学会出版的《妇产科超声》。
Objectives To measure the length, width and area of the urogenital hiatus (UH), and the length and mean echogenicity (MEP) of the puborectalis muscle (PRM), automatically and observer-independently, in the plane of minimal hiatal dimensions on transperineal ultrasound (TPUS) images, by automatic segmentation of the UH and the PRM using deep learning.Methods In 1318 three-and four-dimensional (3D/4D) TPUS volume datasets from 253 nulliparae at 12 and 36weeks' gestation, two-dimensional (2D) images in the plane of minimal hiatal dimensions with the PRM at rest, on maximum contraction and on maximum Valsalva maneuver, were obtained manually and the UH and PRM were segmented manually. In total, 713 of the images were used to train a convolutional neural network (CNN) to segment automatically the UH and PRM in the plane of minimal hiatal dimensions. In the remainder of the dataset (test set 1 (TS1); 601 images, four having been excluded), the performance of the CNN was evaluated by comparing automatic and manual segmentations. The performance of the CNN was also tested on 117 images from an independent dataset (test set 2 (TS2); two images having been excluded) from 40 nulliparae at 12 weeks' gestation, which were acquired and segmented manually by a different observer. The success of automatic segmentation was assessed visually. Based on the CNN segmentations, the following clinically relevant parameters were measured: the length, width and area of the UH, the length of the PRM and MEP. The overlap (Dice similarity index (DSI)) and surface distance (mean absolute distance (MAD) and Hausdorff distance (HDD)) between manual and CNN segmentations were measured to investigate their similarity. For the measured clinically relevant parameters, the intraclass correlation coefficients (ICCs) between manual and CNN results were determined.Results Fully automatic CNN segmentation was successful in 99.0% and 93.2% of images in TS1 and TS2, respectively. DSI, MAD and HDD showed good overlap and distance between manual and CNN segmentations in both test sets. This was reflected in the respective ICC values in TS1 and TS2 for the length (0.96 and 0.95), width (0.77 and 0.87) and area (0.96 and 0.91) of the UH, the length of the PRM (0.87 and 0.73) and MEP (0.95 and 0.97), which showed good to very good agreement.Conclusion Deep learning can be used to segment automatically and reliably the PRM and UH on 2D ultrasound images of the nulliparous pelvic floor in the plane of minimal hiatal dimensions. These segmentations can be used to measure reliably UH dimensions as well as PRM length and MEP. (c) 2018 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.