Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection
  in OCTA Images
Shouyue Liu Jinkui Hao Yanwu Xu Huazhu Fu Xinyu Guo Jiang Liu Yalin Zheng Yonghuai Liu Jiong Zhang Yitian Zhao
Optical Coherence Tomography Angiography (OCTA) is a promising tool for
detecting Alzheimer's disease (AD) by imaging the retinal microvasculature.
Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to
study OCTA image biomarkers and understand the correlation with AD. However,
existing studies have used general deep computer vision methods, which present
challenges in providing interpretable results and leveraging clinical prior
knowledge. To address these challenges, 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.