Direct Estimation of Pupil Parameters Using Deep Learning for Visible Light Pupillometry

TL;DR

This paper introduces a deep learning method to directly estimate pupil parameters from visible light images, improving accuracy and efficiency for portable pupillometry applications in neurology and ophthalmology.

Contribution

The novel approach eliminates the need for separate ellipse fitting by directly predicting pupil parameters from deep learning models, enhancing speed and accuracy.

Findings

Deep learning accurately detects pupil pixels in visible light images.

Direct estimation of pupil ellipse parameters improves processing efficiency.

Preliminary results support potential for smartphone-based pupillometry.

Abstract

Pupil reflex to variations in illumination and associated dynamics are of importance in neurology and ophthalmology. This is typically measured using a near Infrared (IR) pupillometer to avoid Purkinje reflections that appear when strong Visible Light (VL) illumination is present. Previously we demonstrated the use of deep learning techniques to accurately detect the pupil pixels (segmentation binary mask) in case of VL images for performing VL pupillometry. Here, we present a method to obtain the parameters of the elliptical pupil boundary along with the segmentation binary pupil mask. This eliminates the need for an additional, computationally expensive post-processing step of ellipse fitting and also improves segmentation accuracy. Using the time-varying ellipse parameters of pupil, we can compute the dynamics of the Pupillary Light Reflex (PLR). We also present preliminary evaluations of our deep learning algorithms on experimental data. This work is a significant push in our goal to develop and validate a VL pupillometer based on a smartphone that can be used in the field.