  Ultrasound examination for detecting fractures is ideally suited for
Emergency Departments (ED) as it is relatively fast, safe (from ionizing
radiation), has dynamic imaging capability and is easily portable. High
interobserver variability in manual assessment of ultrasound scans has piqued
research interest in automatic assessment techniques using Deep Learning (DL).
Most DL techniques are supervised and are trained on large numbers of labeled
data which is expensive and requires many hours of careful annotation by
experts. In this paper, we propose an unsupervised, domain specific transporter
framework to identify relevant keypoints from wrist ultrasound scans. Our
framework provides a concise geometric representation highlighting regions with
high structural variation in a 3D ultrasound (3DUS) sequence. We also
incorporate domain specific information represented by instantaneous local
phase (LP) which detects bone features from 3DUS. We validate the technique on
3DUS videos obtained from 30 subjects. Each ultrasound scan was independently
assessed by three readers to identify fractures along with the corresponding
x-ray. Saliency of keypoints detected in the image\ are compared against manual
assessment based on distance from relevant features.The transporter neural
network was able to accurately detect 180 out of 250 bone regions sampled from
wrist ultrasound videos. We expect this technique to increase the applicability
of ultrasound in fracture detection.
