Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer’s disease (AD) by imaging the pretinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. In this work, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method
commonly used in clinical practice. Furthermore, Polar-Net incorporates
clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model’s decision making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (LNCS)
Publication statusAccepted/In press - 25 May 2023
Event26th International Conference on Medical Image
Computing and Computer Assisted Intervention: MICCAI
- Vancouver, Canada
Duration: 8 Oct 202312 Oct 2023


Conference26th International Conference on Medical Image
Computing and Computer Assisted Intervention
Abbreviated titleMICCAI

Research Centres

  • Centre for Intelligent Visual Computing Research
  • Data and Complex Systems Research Centre


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