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)

基于深度学习的自动分割方法常不够稳健，本文提出了一种创新的三维分割框架通过卷积神经网络将分割表示为 B 样条显式主动表面，保证了分割结果在三维空间中的平滑性、解剖学合理性，并允许用户精确地编辑三维表面，应用于经阴道腔超声图像中肛门括约肌复合体的三维分割任务，与临床工具作对比实验，结果表明：1）该框架赋予用户显式控制表面轮廓的能力；2）相比于现有临床工具，用户感知的工作负荷通过 NASA-TLX 指数降低了 30%；3）所需的用户时间比现有工具减少了 70%（170 秒），p<0.00001。