  When developing deep neural networks for segmenting intraoperative ultrasound
images, several practical issues are encountered frequently, such as the
presence of ultrasound frames that do not contain regions of interest and the
high variance in ground-truth labels. In this study, we evaluate the utility of
a pre-screening classification network prior to the segmentation network.
Experimental results demonstrate that such a classifier, minimising frame
classification errors, was able to directly impact the number of false positive
and false negative frames. Importantly, the segmentation accuracy on the
classifier-selected frames, that would be segmented, remains comparable to or
better than those from standalone segmentation networks. Interestingly, the
efficacy of the pre-screening classifier was affected by the sampling methods
for training labels from multiple observers, a seemingly independent problem.
We show experimentally that a previously proposed approach, combining random
sampling and consensus labels, may need to be adapted to perform well in our
application. Furthermore, this work aims to share practical experience in
developing a machine learning application that assists highly variable
interventional imaging for prostate cancer patients, to present robust and
reproducible open-source implementations, and to report a set of comprehensive
results and analysis comparing these practical, yet important, options in a
real-world clinical application.
