TY - JOUR
T1 - Deep Learning in Medical Ultrasound - From Image Formation to Image Analysis
AU - Mischi, Massimo
AU - Lediju Bell, Muyinatu A.
AU - Van Sloun, Ruud J.G.
AU - Eldar, Yonina C.
N1 - Funding Information: Prof. Eldar is a member of the Israel Academy of Sciences and Humanities and a fellow of EURASIP. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award, the IEEE/AESS Fred Nathanson Memorial Radar Award, the IEEE Kiyo Tomiyasu Award, the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, and the Wolf Foundation Krill Prize for Excellence in Scientific Research. She is the Editor-in-Chief of Foundations and Trends in Signal Processing. She serves the IEEE on several technical and award committees. Funding Information: Dr. Bell’s awards and honors include the NSF CAREER Award, the NIH Trailblazer Award, and MIT Technology Review’s Innovator Under 35 Award. She also serves as Associate Editor-in-Chief of IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL and as Associate Editor of the IEEE TRANSACTIONS ON MEDICAL IMAGING.
PY - 2020/12
Y1 - 2020/12
N2 - Over the past years, deep learning has established itself as a powerful tool across a broad spectrum of domains. While deep neural networks initially found nurture in the computer vision community, they have quickly spread over medical imaging applications, ranging from image analysis and interpretation to—more recently—image formation and reconstruction. Deep learning is now rapidly gaining attention in the ultrasound community, with many groups around the world exploring a wealth of opportunities to improve ultrasound imaging in several key aspects, ranging from beamforming and compressive sampling to speckle suppression, segmentation, and super-resolution imaging.
AB - Over the past years, deep learning has established itself as a powerful tool across a broad spectrum of domains. While deep neural networks initially found nurture in the computer vision community, they have quickly spread over medical imaging applications, ranging from image analysis and interpretation to—more recently—image formation and reconstruction. Deep learning is now rapidly gaining attention in the ultrasound community, with many groups around the world exploring a wealth of opportunities to improve ultrasound imaging in several key aspects, ranging from beamforming and compressive sampling to speckle suppression, segmentation, and super-resolution imaging.
UR - http://www.scopus.com/inward/record.url?scp=85097233759&partnerID=8YFLogxK
U2 - 10.1109/TUFFC.2020.3026598
DO - 10.1109/TUFFC.2020.3026598
M3 - كلمة العدد
SN - 0885-3010
VL - 67
SP - 2477
EP - 2480
JO - IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
JF - IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
IS - 12
M1 - 9269304
ER -