Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data

• Published in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control Vol. 67; no. 12; pp. 2493 - 2509
• Main Authors: Nair, Arun Asokan, Washington, Kendra N., Tran, Trac D., Reiter, Austin, Lediju Bell, Muyinatu A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Array signal processing; Artificial neural networks; Automation; Beamforming; Biomedical materials; Breast - diagnostic imaging; Breast Neoplasms - diagnostic imaging; Cysts; Datasets; Decoding; Deep Learning; Female; Field of view; fully convolutional neural network (FCNN); Humans; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Image segmentation; In vivo methods and tests; Machine learning; neural network; Phantoms, Imaging; Plane waves; Simulation; Task analysis; Training; Ultrasonic imaging; Ultrasonography - methods; Ultrasound
• ISSN: 0885-3010; 1525-8955
• DOI: 10.1109/TUFFC.2020.2993779

Single plane wave transmissions are promising for automated imaging tasks requiring high ultrasound frame rates over an extended field of view. However, a single plane wave insonification typically produces suboptimal image quality. To address this limitation, we are exploring the use of deep neural networks (DNNs) as an alternative to delay-and-sum (DAS) beamforming. The objectives of this work are to obtain information directly from raw channel data and to simultaneously generate both a segmentation map for automated ultrasound tasks and a corresponding ultrasound B-mode image for interpretable supervision of the automation. We focus on visualizing and segmenting anechoic targets surrounded by tissue and ignoring or deemphasizing less important surrounding structures. DNNs trained with Field II simulations were tested with simulated, experimental phantom, and in vivo data sets that were not included during training. With unfocused input channel data (i.e., prior to the application of receive time delays), simulated, experimental phantom, and in vivo test data sets achieved mean ± standard deviation Dice similarity coefficients of 0.92 ± 0.13, 0.92 ± 0.03, and 0.77 ± 0.07, respectively, and generalized contrast-to-noise ratios (gCNRs) of 0.95 ± 0.08, 0.93 ± 0.08, and 0.75 ± 0.14, respectively. With subaperture beamformed channel data and a modification to the input layer of the DNN architecture to accept these data, the fidelity of image reconstruction increased (e.g., mean gCNR of multiple acquisitions of two in vivo breast cysts ranged 0.89-0.96), but DNN display frame rates were reduced from 395 to 287 Hz. 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.