Automated ultrasound assessment of amniotic fluid index using deep learning

Author:

Cho Hyun Cheol,Sun Siyu,Min Hyun Chang,Kwon Ja-Young,Kim Bukweon,Park YejinORCID,Seo Jin Keun

Funder

Samsung Advanced Institute of Technology

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference49 articles.

1. Amniotic fluid water dynamics;Beall;Placenta,2007

2. Ultrasound evaluation of amniotic fluid volume: I. The relationship of marginal and decreased amniotic fluid volumes to perinatal outcome;Chamberlain;Am. J. Obstet. Gynecol.,1984

3. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs;Chen;IEEE Trans. Pattern Anal. Mach. Intell.,2017

4. Chen, L.-C., Papandreou, G., Schroff, F., Adam, H., 2017b. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587

5. Encoder-decoder with atrous separable convolution for semantic image segmentation;Chen,2018

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