Minuscule Cell Detection in AS-OCT Images with Progressive Field-of-View Focusing
Boyu Chen, Ameenat L. Solebo, Daqian Shi, Jinge Wu, Paul Taylor
TL;DR
This work tackles the challenge of detecting minuscule inflammatory cells in high-resolution AS-OCT images for anterior uveitis. It introduces a progressive field-of-view strategy implemented as the Minuscule Cell Detection (MCD) framework, combining a zero-shot Field-of-Focus AC segmentation (FoF) with a Fine-grained Object Detection (FOD) pipeline that uses Minuscule Region Proposals (MiRP) and a Spatial Attention Network. The approach achieves state-of-the-art performance in AC segmentation and cell detection, with robust counting accuracy and localization under low IoU thresholds, outperforming threshold-based and DL baselines. By enabling accurate cell quantification and spatial analysis, the method holds promise for improved monitoring of disease progression and can generalize to other high-resolution medical imaging tasks requiring precise detection of extremely small objects.
Abstract
Anterior Segment Optical Coherence Tomography (AS-OCT) is an emerging imaging technique with great potential for diagnosing anterior uveitis, a vision-threatening ocular inflammatory condition. A hallmark of this condition is the presence of inflammatory cells in the eye's anterior chamber, and detecting these cells using AS-OCT images has attracted research interest. While recent efforts aim to replace manual cell detection with automated computer vision approaches, detecting extremely small (minuscule) objects in high-resolution images, such as AS-OCT, poses substantial challenges: (1) each cell appears as a minuscule particle, representing less than 0.005\% of the image, making the detection difficult, and (2) OCT imaging introduces pixel-level noise that can be mistaken for cells, leading to false positive detections. To overcome these challenges, we propose a minuscule cell detection framework through a progressive field-of-view focusing strategy. This strategy systematically refines the detection scope from the whole image to a target region where cells are likely to be present, and further to minuscule regions potentially containing individual cells. Our framework consists of two modules. First, a Field-of-Focus module uses a vision foundation model to segment the target region. Subsequently, a Fine-grained Object Detection module introduces a specialized Minuscule Region Proposal followed by a Spatial Attention Network to distinguish individual cells from noise within the segmented region. Experimental results demonstrate that our framework outperforms state-of-the-art methods for cell detection, providing enhanced efficacy for clinical applications. Our code is publicly available at: https://github.com/joeybyc/MCD.