DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces
Helena Williams João Pedrosa Muhammad Asad Laura Cattani Tom Vercauteren Jan Deprest Jan D'hooge
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)