Anterior uveitis, a common form of eye inflammation, can lead to permanent
vision loss if not promptly diagnosed. Monitoring this condition involves
quantifying inflammatory cells in the anterior chamber (AC) of the eye, which
can be captured using Anterior Segment Optical Coherence Tomography (AS-OCT).
However, manually identifying cells in AS-OCT images is time-consuming and
subjective. Moreover, existing automated approaches may have limitations in
both the effectiveness of detecting cells and the reliability of their
detection results. To address these challenges, we propose an automated
framework to detect cells in the AS-OCT images. This framework consists of a
zero-shot chamber segmentation module and a cell detection module. The first
module segments the AC area in the image without requiring human-annotated
training data. Subsequently, the second module identifies individual cells
within the segmented AC region. Through experiments, our framework demonstrates
superior performance compared to current state-of-the-art methods for both AC
segmentation and cell detection tasks. Notably, we find that previous cell
detection approaches could suffer from low recall, potentially overlooking a
significant number of cells. In contrast, our framework offers an improved
solution, which could benefit the diagnosis and study of anterior uveitis. Our
code for cell detection is publicly available at:
this https URL

自动化框架在前房光学相干断层扫描图像中检测细胞，实现超越当前技术的性能，从而改善前睫病的诊断和研究。

前段眼节光学相干断层扫描图像中的细胞检测进展

Advancing Cell Detection in Anterior Segment Optical Coherence  Tomography Images

This paper presents the development and validation of a Generative
Adversarial Network (GAN) purposed to create high-resolution, realistic
Anterior Segment Optical Coherence Tomography (AS-OCT) images. We trained the
Style and WAvelet based GAN (SWAGAN) on 142,628 AS-OCT B-scans. Three
experienced refractive surgeons performed a blinded assessment to evaluate the
realism of the generated images; their results were not significantly better
than chance in distinguishing between real and synthetic images, thus
demonstrating a high degree of image realism. To gauge their suitability for
machine learning tasks, a convolutional neural network (CNN) classifier was
trained with a dataset containing both real and GAN-generated images. The CNN
demonstrated an accuracy rate of 78% trained on real images alone, but this
accuracy rose to 100% when training included the generated images. This
underscores the utility of synthetic images for machine learning applications.
We further improved the resolution of the generated images by up-sampling them
twice (2x) using an Enhanced Super Resolution GAN (ESRGAN), which outperformed
traditional up-sampling techniques. In conclusion, GANs can effectively
generate high-definition, realistic AS-OCT images, proving highly beneficial
for machine learning and image analysis tasks.

本文介绍了开发和验证了一种生成对抗网络（GAN），旨在创建高分辨率，逼真的前节光学相干层析成像（AS-OCT）图像。作者使用经验丰富的屈光外科医生对这些生成的图像的逼真程度进行了评估，并证明了他们的实用性。此外，使用提高后的分辨率的生成图像进一步改善了它们的质量，增强了它们在机器学习和图像分析任务中的有效性。