Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

Ultrasonography

Jae-Hyun Kwon¹  Won-Chul Bang²  Dong Woo Kim³  Kilsu Ha³  Byungeun Ahn³  Zaegyoo Hah⁴  Jinyong Lee⁴  Kang-Sik Kim⁵  Ho Kyung Kang⁵  Moon Ho Park⁵  Yeong Kyeong Seong⁵  Jonghyon Yi⁵ 

[1] DR Imaging R&D Lab, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea;Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Korea;Product Strategy Group, Samsung Medison Co., Ltd., Seongnam, Korea;System R&D Group, Samsung Medison Co., Ltd., Seongnam, Korea;Ultrasound R&D Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seongnam, Korea;

关键词: deep learning;    convolutional neural network;    artificial intelligence;    computer-aided diagnosis;    workflow efficiency;   

DOI  :  10.14366/usg.20102

来源: DOAJ

【 摘 要 】

In this review of the most recent applications of deep learning to ultrasound imaging, the architectures of deep learning networks are briefly explained for the medical imaging applications of classification, detection, segmentation, and generation. Ultrasonography applications for image processing and diagnosis are then reviewed and summarized, along with some representative imaging studies of the breast, thyroid, heart, kidney, liver, and fetal head. Efforts towards workflow enhancement are also reviewed, with an emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented regarding image quality enhancement, diagnostic support, and improvements in workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.

【 授权许可】

Unknown