Boundary-aware uncertainty for automatic caliper placement

Rachana Sathish; Rahul Venkataramani; Chandan Aladahalli; K. S. Shriram; Prasad Sudhakar

doi:10.1117/12.3007645

2 April 2024 Boundary-aware uncertainty for automatic caliper placement

Rachana Sathish, Rahul Venkataramani, Chandan Aladahalli, K. S. Shriram, Prasad Sudhakar

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292615 (2024) https://doi.org/10.1117/12.3007645
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Rachana Sathish, Rahul Venkataramani, Chandan Aladahalli, K. S. Shriram, and Prasad Sudhakar "Boundary-aware uncertainty for automatic caliper placement", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292615 (2 April 2024); https://doi.org/10.1117/12.3007645

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