AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images.

Journal: Translational vision science & technology
PMID: 35938881

Abstract

PURPOSE: Accurate segmentation of microaneurysms (MAs) from adaptive optics scanning laser ophthalmoscopy (AOSLO) images is crucial for identifying MA morphologies and assessing the hemodynamics inside the MAs. Herein, we introduce AOSLO-net to perform automatic MA segmentation from AOSLO images of diabetic retinas.

Authors

  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.
  • Konstantina Sampani
    Beetham Eye Institute, Joslin Diabetes Center, Boston, MA, USA.
  • Mengjia Xu
    Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
  • Shengze Cai
    Division of Applied Mathematics, Brown University, Providence, RI 02912.
  • Yixiang Deng
    School of Engineering, Brown University, Providence, RI, USA.
  • He Li
    National Soybean Processing Industry Technology Innovation Center, Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University Beijing 100048 China lihe@btbu.edu.cn liuxinqi@btbu.edu.cn.
  • Jennifer K Sun
    Beetham Eye Institute, Joslin Diabetes Center, Boston, MA, USA.
  • George Em Karniadakis
    Division of Applied Mathematics, Brown University, Providence, RI 02912, USA.

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