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.

我们提出了一种名为 Polar-Net 的新型深度学习框架，将 OCTA 图像从笛卡尔坐标系映射到极坐标系，实现了基于 ETDRS 网格的区域分析方法，并结合临床先验信息提高性能，这有助于了解模型在检测 AD 时的决策过程和其与临床观察的一致性。通过对私有和公共数据集的评估，我们证明了 Polar-Net 优于现有的最先进方法，并为视网膜血管变化与 AD 之间的关联提供了更有价值的病理证据。

Polar-Net：一种用于 OCTA 图像中阿尔茨海默病检测的临床友好模型

Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection  in OCTA Images

The retina is the only part of the human body in which blood vessels can be
accessed non-invasively using imaging techniques such as digital fundus images
(DFI). The spatial distribution of the retinal microvasculature may change with
cardiovascular diseases and thus the eyes may be regarded as a window to our
hearts. Computerized segmentation of the retinal arterioles and venules (A/V)
is essential for automated microvasculature analysis. Using active learning, we
created a new DFI dataset containing 240 crowd-sourced manual A/V segmentations
performed by fifteen medical students and reviewed by an ophthalmologist, and
developed LUNet, a novel deep learning architecture for high resolution A/V
segmentation. LUNet architecture includes a double dilated convolutional block
that aims to enhance the receptive field of the model and reduce its parameter
count. Furthermore, LUNet has a long tail that operates at high resolution to
refine the segmentation. The custom loss function emphasizes the continuity of
the blood vessels. LUNet is shown to significantly outperform two
state-of-the-art segmentation algorithms on the local test set as well as on
four external test sets simulating distribution shifts across ethnicity,
comorbidities, and annotators. We make the newly created dataset open access
(upon publication).

利用眼底数字成像技术，通过主动学习构建了一个新的数据集，其中包含由医学生及眼科医师审查的 240 个手动分割的视网膜小动脉和小静脉，开发了一种名为 LUNet 的新型深度学习架构，用于高分辨率的动静脉分割，并证明 LUNet 在多个测试集上显著优于其他分割算法。

LUNet：深度学习用于高分辨率眼底图像的细动脉和细静脉分割

LUNet: Deep Learning for the Segmentation of Arterioles and Venules in  High Resolution Fundus Images

Optical coherence tomography angiography (OCTA) is a new imaging modality to
visualize retinal microvasculature and has been readily adopted in clinics.
High-resolution OCT angiograms are important to qualitatively and
quantitatively identify potential biomarkers for different retinal diseases
accurately. However, one significant problem of OCTA is the inevitable decrease
in resolution when increasing the field-of-view given a fixed acquisition time.
To address this issue, we propose a novel reference-based super-resolution
(RefSR) framework to preserve the resolution of the OCT angiograms while
increasing the scanning area. Specifically, textures from the normal RefSR
pipeline are used to train a learnable texture generator (LTG), which is
designed to generate textures according to the input. The key difference
between the proposed method and traditional RefSR models is that the textures
used during inference are generated by the LTG instead of being searched from a
single reference image. Since the LTG is optimized throughout the whole
training process, the available texture space is significantly enlarged and no
longer limited to a single reference image, but extends to all textures
contained in the training samples. Moreover, our proposed LTGNet does not
require a reference image at the inference phase, thereby becoming invulnerable
to the selection of the reference image. Both experimental and visual results
show that LTGNet has superior performance and robustness over state-of-the-art
methods, indicating good reliability and promise in real-life deployment. The
source code will be made available upon acceptance.

提出了一种基于参考的超分辨率（RefSR）框架，用于保留 OCT 血管造影图的分辨率并扩大扫描区域，通过使用训练出的纹理生成器生成纹理，提高了精度和稳健性，具有应用前景。