Protocol for performing deep learning-based fundus fluorescein angiography image analysis with classification and segmentation tasks.

Journal: STAR protocols
Published Date: Jun 19, 2024

Abstract

Fundus fluorescein angiography (FFA) examinations are widely used in the evaluation of fundus disease conditions to facilitate further treatment suggestions. Here, we present a protocol for performing deep learning-based FFA image analytics with classification and segmentation tasks. We describe steps for data preparation, model implementation, statistical analysis, and heatmap visualization. The protocol is applicable in Python using customized data and can achieve the whole process from diagnosis to treatment suggestion of ischemic retinal diseases. For complete details on the use and execution of this protocol, please refer to Zhao et al..

Authors

  • Zhenzhe Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Xinyu Zhao
    AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
  • Shanshan Yu
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Liqiong Xie
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
  • Yue Xu
  • Lanqin Zhao
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong, 510060, China.
  • Guoming Zhang
    Shenzhen Eye Hospital; Shenzhen Key Ophthalmic Laboratory, Health Science Center, Shenzhen University, The Second Affiliated Hospital of Jinan University, Shenzhen, China. Electronic address: 13823509060@163.com.
  • Shaochong Zhang
    Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen 518040, China.
  • Yan Lu
    National Institute of Standards and Technology.
  • Haotian Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou.
  • Xiaoling Liang
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
  • Duoru Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.

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