基于有限训练样本的融合网络模型用于盆腔危及器官自动分割的研究

将深度学习应用到医学影像中危及器官自动分割领域时,为解决训练样本不足时三维卷积神经网络优化出现的退化、梯度消失等问题,本研究将Dense Net与V-Net两个网络模型进行融合,开发一种用于三维计算机断层扫描(CT)图像自动分割的Dense V-Network算法,勾画女性盆腔危及器官。采用戴斯相似性系数(DSC)、豪斯多夫距离(HD)、杰卡德距离(JD)三个参数来定量评估分割效果。结果显示膀胱、小肠、直肠、股骨头和脊髓自动分割的DSC值均在0.87以上(平均值是0.9);JD值均在2.3以内(平均值是0.18);除小肠外,HD值均在0.9 cm以内(平均值是0.62 cm)。经验证,Dense V-Network网络可精准地勾画盆腔危及器官。

A fusion network model based on limited training samples for the automatic segmentation of pelvic endangered organs

When applying deep learning to the automatic segmentation of organs at risk in medical images, we combine two network models of Dense Net and V-Net to develop a Dense V-network for automatic segmentation of three-dimensional computed tomography(CT) images, in order to solve the problems of degradation and gradient disappearance of three-dimensional convolutional neural networks optimization as training samples are insufficient. This algorithm is applied to the delineation of pelvic endangered organs and we take three representative evaluation parameters to quantitatively evaluate the segmentation effect. The clinical result showed that the Dice similarity coefficient values of the bladder, small intestine, rectum, femoral head and spinal cord were all above 0.87(average was 0.9);Jaccard distance of these were within 2.3(average was 0.18). Except for the small intestine, the Hausdorff distance of other organs were less than 0.9 cm(average was 0.62 cm). The Dense V-Network has been proven to achieve the accurate segmentation of pelvic endangered organs.