Congratulations to Arun Nair! His first-author journal paper entitled Deep learning to obtain simultaneous ultrasound image and segmentation outputs from a single input of raw channel data was accepted to IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. The paper will appear in the journal’s special issue on Deep Learning in Medical Ultrasound – from image formation to image analysis.
This paper explores the use of deep neural network (DNNs) as alternatives to delay-and-sum beamforming. The DNNs learned information directly from raw channel data to simultaneously generate both a segmentation map for automated ultrasound tasks and a corresponding ultrasound B-mode image for interpretable supervision of the automation. Although the focus was visualization and segmentation of anechoic targets surrounded by tissue, the concept can be adapted to any specific task of interest. Overall, the DNNs successfully translated feature representations learned from simulated data to phantom and in vivo data, which is promising for this novel approach to simultaneous ultrasound image formation and segmentation.
Citation: Nair AA, Washington K, Tran T, Reiter A, Bell MAL, Deep learning to obtain simultaneous ultrasound image and segmentation outputs from a single input of raw channel data, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (accepted May 6, 2020) [pdf]