Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency
Jonghyon Yi1 , Ho Kyung Kang1, Jae-Hyun Kwon2 , Kang-Sik Kim1, Moon Ho Park1, Yeong Kyeong Seong1 , DongWoo Kim3, ByungEun Ahn3, Kilsu Ha3, Jinyong Lee4 , Zaegyoo Hah4, Won-Chul Bang5
1Ultrasound R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam-si, Korea
2DR Imaging R&D Lab, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam-si, Korea
3Product Strategy Group, Samsung Medison Co., Ltd., Seongnam-si, Korea
4System R&D Group, Samsung Medison Co., Ltd, Seongnam-si, Korea
5Product Strategy Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam-si, Korea
6Samsung Electronics, Seongnam-si, Gyeonggi-do, 13530, Korea
Corresponding Author: Won-Chul Bang ,Tel: +82-10-6405-8019, Fax: +10-2-2194-0899, Email: wcbang@samsung.com
Received: July 3, 2020;  Accepted: September 14, 2020.  Published online: September 14, 2020.
ABSTRACT
Reviews of the most recent applications of deep learning on ultrasound imaging applications are presented. Architectures of deep learning networks are briefly explained for medical imaging application categories of classification, detection, segmentation, and generation. Ultrasonography applications are then reviewed and summarized for image processing and diagnosis along with some representative study cases of breast, thyroid, heart, kidney, liver, and fetal head. Efforts on workflow enhancement are also reviewed with emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented on image quality Enhancement, diagnostic support, and improving workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.
Keywords: Deep learning; Convolutional neural network; Artificial intelligence; Computer-aided diagnosis; Workflow efficiency
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