  Automatic medical image segmentation via convolutional neural networks (CNNs)
has shown promising results. However, they may not always be robust enough for
clinical use. Sub-optimal segmentation would require clinician's to manually
delineate the target object, causing frustration. To address this problem, a
novel interactive CNN-based segmentation framework is proposed in this work.
The aim is to represent the CNN segmentation contour as B-splines by utilising
B-spline explicit active surfaces (BEAS). The interactive element of the
framework allows the user to precisely edit the contour in real-time, and by
utilising BEAS it ensures the final contour is smooth and anatomically
plausible. This framework was applied to the task of 2D segmentation of the
levator hiatus from 2D ultrasound (US) images, and compared to the current
clinical tools used in pelvic floor disorder clinic (4DView, GE Healthcare;
Zipf, Austria). Experimental results show that: 1) the proposed framework is
more robust than current state-of-the-art CNNs; 2) the perceived workload
calculated via the NASA-TLX index was reduced more than half for the proposed
approach in comparison to current clinical tools; and 3) the proposed tool
requires at least 13 seconds less user time than the clinical tools, which was
significant (p=0.001).
