Deep learning-based automatic segmentation methods have become state-of-the-art. However, they are often not robust enough for direct clinical application, as domain shifts between training and testing data affect their performance. Failure in automatic segmentation can cause sub-optimal results that require correction. To address these problems, we propose a novel 3D extension of an interactive segmentation framework that represents a segmentation from a convolutional neural network (CNN) as a B-spline explicit active surface (BEAS). BEAS ensures segmentations are smooth in 3D space, increasing anatomical plausibility, while allowing the user to precisely edit the 3D surface. We apply this framework to the task of 3D segmentation of the anal sphincter complex (AS) from transperineal ultrasound (TPUS) images, and compare it to the clinical tool used in the pelvic floor disorder clinic (4D View VOCAL, GE Healthcare; Zipf, Austria). Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 7 0% (170 seconds) less user time than VOCAL (p< 0.00001)

翻译：基于深度学习的自动分割方法已成为当前最先进的技术。然而，由于训练数据与测试数据之间存在领域偏移，这些方法在直接临床应用中往往不够鲁棒。自动分割的失败可能导致需要修正的次优结果。为解决这些问题，我们提出了一种交互式分割框架的新型三维扩展方法，该框架将卷积神经网络（CNN）的分割结果表示为B样条显式活动表面（BEAS）。BEAS确保分割在三维空间中保持平滑，提升解剖学合理性，同时允许用户精确编辑三维表面。我们将该框架应用于经会阴超声（TPUS）图像中肛门括约肌复合体（AS）的三维分割任务，并与盆底障碍疾病临床实践中使用的工具（4D View VOCAL，GE Healthcare；奥地利Zipf）进行比较。实验结果表明：1）所提出的框架允许用户显式控制表面轮廓；2）通过NASA-TLX指数计算的主观工作量较VOCAL减少了30%；3）用户操作时间较VOCAL减少了70%（170秒）（p<0.00001）。