Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia.

Journal: Journal of diabetes research
PMID: 35257013

Abstract

OBJECTIVES: The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantification of the FAZ in DMI, using an improved U-Net convolutional neural network (CNN) and to establish a CNN model based on optical coherence tomography angiography (OCTA) images for the same purpose.

Authors

  • Yongan Meng
    Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha 410011, China.
  • Hailei Lan
    School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Yuqian Hu
    Department of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Zailiang Chen
    School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
  • Pingbo Ouyang
    Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha 410011, China.
  • Jing Luo
    Department of Ophthalmology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin RD, Changsha, Hunan, China.

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