Exploiting acquisition path to detect shadows in ultrasound images
Vikram R. Melapudi, Chandan Aladahalli, K. S. Shriram, Hsi-Ming Chang · 2022
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.