Purpose: To describe SLOctolyzer: an open-source analysis toolkit for en face
retinal vessels appearing in infrared reflectance scanning laser ophthalmoscopy
(SLO) images.
Methods: SLOctolyzer includes two main modules: segmentation and measurement.
The segmentation module use deep learning methods to delineate retinal anatomy,
while the measurement module quantifies key retinal vascular features such as
vessel complexity, density, tortuosity, and calibre. We evaluate the
segmentation module using unseen data and measure its reproducibility.
Results: SLOctolyzer's segmentation module performed well against unseen
internal test data (Dice for all-vessels, 0.9097; arteries, 0.8376; veins,
0.8525; optic disc, 0.9430; fovea, 0.8837). External validation against severe
retinal pathology showed decreased performance (Dice for arteries, 0.7180;
veins, 0.7470; optic disc, 0.9032). SLOctolyzer had good reproducibility (mean
difference for fractal dimension, -0.0007; vessel density, -0.0003; vessel
calibre, -0.3154 $\mu$m; tortuosity density, 0.0013). SLOctolyzer can process a
macula-centred SLO image in under 20 seconds and a disc-centred SLO image in
under 30 seconds using a standard laptop CPU.
Conclusions: To our knowledge, SLOctolyzer is the first open-source tool to
convert raw SLO images into reproducible and clinically meaningful retinal
vascular parameters. SLO images are captured simultaneous to optical coherence
tomography (OCT), and we believe our software will be useful for extracting
retinal vascular measurements from large OCT image sets and linking them to
ocular or systemic diseases. It requires no specialist knowledge or proprietary
software, and allows manual correction of segmentations and re-computing of
vascular metrics. SLOctolyzer is freely available at
this https URL

SLOctolyzer 是第一个开源工具，用于将原始 SLO 图像转换为可复制和临床意义的视网膜血管参数，并从大型 OCT 图像集中提取视网膜血管测量结果，并将其与眼部或系统性疾病联系起来。

SLOctolyzer：扫描激光眼底图像分割和特征提取的全自动分析工具包

SLOctolyzer: Fully automatic analysis toolkit for segmentation and  feature extracting in scanning laser ophthalmoscopy images

Blinding eye diseases are often correlated with altered retinal morphology,
which can be clinically identified by segmenting retinal structures in fundus
images. However, current methodologies often fall short in accurately
segmenting delicate vessels. Although deep learning has shown promise in
medical image segmentation, its reliance on repeated convolution and pooling
operations can hinder the representation of edge information, ultimately
limiting overall segmentation accuracy. In this paper, we propose a lightweight
pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an
exceptionally low number of learnable parameters \textbf{(only 0.172 M)}. The
network used multipath feature extraction blocks and incorporates bidirectional
skip connections for the information flow between the encoder and decoder.
Additionally, we have optimized the efficiency of the model by carefully
selecting the number of filters to avoid filter overlap. This optimization
significantly reduces training time and enhances computational efficiency. To
assess the robustness and generalizability of LMBiS-Net, we performed
comprehensive evaluations on various aspects of retinal images. Specifically,
the model was subjected to rigorous tests to accurately segment retinal
vessels, which play a vital role in ophthalmological diagnosis and treatment.
By focusing on the retinal blood vessels, we were able to thoroughly analyze
the performance and effectiveness of the LMBiS-Net model. The results of our
tests demonstrate that LMBiS-Net is not only robust and generalizable but also
capable of maintaining high levels of segmentation accuracy. These
characteristics highlight the potential of LMBiS-Net as an efficient tool for
high-speed and accurate segmentation of retinal images in various clinical
applications.

通过提出一种名为 LMBiS-Net 的轻量级像素级卷积神经网络，该论文在仅有 0.172 M 的可学习参数下，成功应用于眼科诊断和治疗中，以实现对视网膜图像的快速准确分割。

LMBiS-Net: 一种基于轻量级多路径双向跳跃连接的卷积神经网络用于视网膜血管分割

LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based  CNN for Retinal Blood Vessel Segmentation

Diseases such as diabetic retinopathy and age-related macular degeneration
pose a significant risk to vision, highlighting the importance of precise
segmentation of retinal vessels for the tracking and diagnosis of progression.
However, existing vessel segmentation methods that heavily rely on
encoder-decoder structures struggle to capture contextual information about
retinal vessel configurations, leading to challenges in reconciling semantic
disparities between encoder and decoder features. To address this, we propose a
novel feature enhancement segmentation network (FES-Net) that achieves accurate
pixel-wise segmentation without requiring additional image enhancement steps.
FES-Net directly processes the input image and utilizes four prompt
convolutional blocks (PCBs) during downsampling, complemented by a shallow
upsampling approach to generate a binary mask for each class. We evaluate the
performance of FES-Net on four publicly available state-of-the-art datasets:
DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the
superior performance of FES-Net compared to other competitive approaches
documented in the existing literature.

利用深度学习技术，提出了一种名为 FES-Net 的新型特征增强分割网络，用于准确地分割视网膜血管，无需额外的图像增强步骤，并在多个公开数据集上验证了其卓越的性能。