  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:
https://github.com/joeybyc/cell_detection.
