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The application of deep learning framework in quantifying retinal structures on ophthalmic image in research eye-PACS
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Paper Abstract

The rise of deep learning (DL) framework and its application in object recognition could benefit image-based medical diagnosis. Since eye is believed to be a window into human health, the application of DL on differentiating abnormal ophthalmic photography (OP) will greatly empower ophthalmologists to relieve their workload for disease screening. In our previous work, we employed ResNet-50 to construct classification model for diabetic retinopathy(DR) within the PACS. In this study, we implemented latest DL object detection and semantic segmentation framework to empower the eye-PACS. Mask R-CNN framework was selected for object detection and instance segmentation of the optic disc (OD) and the macula. Furthermore, Unet framework was utilized for semantic segmentation of retinal vessel pixels from OP. The performance of the segmented results by two frameworks achieved state-of-art efficiency and the segmented results were transmitted to PACS as grayscale softcopy presentation state (GSPS) file. We also developed a prototype for OP quantitative analysis. It’s believed that the implementation of DL framework into the object recognition and analysis on OPs is meaningful and worth further investigation.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 1095402 (15 March 2019); doi: 10.1117/12.2512458
Show Author Affiliations
Moye Yu, Medical School of Nantong Univ. (China)
Siliang Zhang, The Univ. of Southern California (United States)
Brent J. Liu, The Univ. of Southern California (United States)
Shenghui Zhao, Medical School of Nantong Univ. (China)
Aimin Sang, Affiliated Hospital of Nantong Univ. (China)
Jiancheng Dong, Medical School of Nantong Univ. (China)
Huiqun Wu, Medical School of Nantong Univ. (China)
Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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