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.
Shadow artefacts in Ultrasound make clinical interpretation of the image difficult and even impossible in certain scenarios. Shadow detection and avoidance is therefore a very important feature for automatic interpretation Ultrasound images. Deep Learning (DL) based methods for automatic shadow detection have approached it as a segmentation problem achieving limited accuracy. Since that acoustic shadows appear along the acquisition path, we propose a novel approach of extracting slivers of images called fanlets along the acquisition path and employ a simpler classification approach to detect presence of shadows. Limiting the spatial context for shadow detection helps us to achieve a very high accuracy of 97%. On a database of abdominal ultrasound videos from 128 subjects, we show that our approach is superior to UNet based shadow segmentation. Since any Ultrasound image can be broken into a series of fanlets, our approach can be readily applied to a wide variety of acquisitions.
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