Boundary-Aware Uncertainty for Automatic Caliper Placement

Caliper placement is an integral part of ultrasound clinical workflow, e.g., kidney volume measurement. Automated approaches utilize anatomical segmentation followed by application-specific caliper placement. Robust clinical outcomes require confidence/uncertainty associated with such predictions be indicated. Conventional methods estimating uncertainty (MC Dropout, Deep Ensembles) with high computational load are impractical for deployment. We exploit the existence of uncertainty only on boundary pixels for any predicted segmentation. We utilize disagreement between independent predictions - region segmentation edge and direct boundary prediction, to identify uncertainty on anatomical boundary. We demonstrate our Boundary-Aware Segmentation Uncertainty (BASU) on cross-sections of kidney, correlating with ground-truth and clinician's intuitions.