Exploiting Acoustic Path to Detect Shadows in Ultrasound Images

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.