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Pupillometry is a non-invasive technique that can be used to objectively characterize pathophysiological changes involving the pupillary reflex. It is essentially the measurement of the pupil diameter over time. Here, specially designed computer algorithms provide fast, reliable and reproducible solutions for the analysis. These methods use a priori information about the shape and color of the pupil. Our study focuses on measuring the diameter and dynamics of the pupils of rats with schizophrenia using videos recorded with a modified digital camera under infrared (IR) illumination. We developed a novel, robust method that measures the size of a pupil even under poor circumstances (noise, blur, reflections and occlusions). We compare our results with measurements obtained using manual annotation.

The investigation of the pupillary light reflex (PLR) is a well-known method to provide information about the functionality of the autonomic nervous system. Pupillometry, a non-invasive technique, was applied to study the PLR alterations in a new, schizophrenia-like rat substrain, named WISKET. The pupil responses to light impulses were recorded with an infrared camera; the videos were automatically processed and features were extracted. Besides the classical statistical analysis (ANOVA), feature selection and classification were applied to reveal the significant differences in the PLR parameters between the control and WISKET animals. Based on these results, the disadvantages of this method were analyzed and the measurement process was redesigned and improved. The automated pupil detection method has also been adapted to the new videos. 2564 images were annotated manually and used to train a fully-convolutional neural network to produce pupil mask images. The method was evaluated on 329 test images and achieved 4% median relative error. With the new setup, the pupil detection became reliable and the new data acquisition offers robustness to the experiments.