Segmentation of the levator hiatus in ultrasound allows to extract biometrics which are of importance for pelvic floor disorder assessment. In this work, we present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a 2D image extracted from a 3D ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalising activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction and rest, all labelled by three operators, is used for training and evaluation in a leave-one-patient-out cross-validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams' index of 1.03), and outperforming a U-Net architecture without the need for batch normalisation. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semi-automatic approach.
翻译:超声波中悬浮体间断层的悬浮体分解使得能够提取对骨盆底层失调评估具有重要性的生物鉴别技术。 在这项工作中,我们展示了一种完全自动的方法,使用一个卷发神经网络(CNN)来勾画从3D超声波体积中提取的2D图像中的悬浮体间断层。特别是,我们的方法使用最近开发的不线性指数线性单位(SELU)作为非线性自我调节激活功能,首次在CNN的医疗成像中应用。 SELU有重要优势,如无参数和小型独立,这可能有助于克服培训过程中的记忆限制。由3个操作者标注的由Valsalva、收缩和休息期间35名病人的91个图像组成的数据集,用于在请假1-住院出交叉校验时进行训练和评价。结果显示一个中位的Dice类似系数为0.90,中间方位范围为0.08,与3个操作者相当(Williams的索引为1.03),并且比平等的U型平面平面结构结构结构,无需完成正常的平面结构。