Identification of Retinal Ganglion Cells from β-III Stained Fluorescent Microscopic Images.

Optic nerve crush in mouse model is widely used for investigating the course following retinal ganglion cell (RGCs) injury. Manual cell counting from β-III tubulin stained microscopic images has been routinely performed to monitor RGCs after an optic nerve crush injury, but is time-consuming and prone to observer variability. This paper describes an automatic technique for RGC identification. We developed and validated (i) a sensitive cell candidate segmentation scheme and (ii) a classifier that removed false positives while retaining true positives. Two major contributions were made in cell candidate segmentation. First, a homomorphic filter was designed to adjust for the inhomogeneous illumination caused by uneven penetration of β-III tubulin antibody. Second, the optimal segmentation parameters for cell detection are highly image-specific. To address this issue, we introduced an offline-online parameter tuning approach. Offline tuning optimized model parameters based on training images and online tuning further optimized the parameters at the testing stage without needing access to the ground truth. In the cell identification stage, 31 geometric, statistical and textural features were extracted from each segmented cell candidate, which was subsequently classified as true or false positives by support vector machine. The homomorphic filter and the online parameter tuning approach together increased cell recall by 28%. The entire pipeline attained a recall, precision and coefficient of determination (r2) of 85.3%, 97.1% and 0.994. The availability of the proposed pipeline will allow efficient, accurate and reproducible RGC quantification required for assessing the death/survival of RGCs in disease models.