Automatic pupil detection using off-axis iris images for alignment guidance in fundus cameras

Abstract

Purpose : The purpose of this study is to create a pupil tracking algorithm to find the center of the pupil within 400 micrometers of ground truth (manual annotations) in non-mydriatic external eye images. Pupil detection is crucial for automation and alignment guidance, which can improve the quality of fundus image acquisitions.

Methods : In the ultra-widefield fundus imaging system CLARUS^TM 500 (ZEISS, Dublin, CA), two iris cameras provide an off-axis view of the patient’s eye along with the position of the pupil within the field of view. In this retrospective study, we used 654 external eye images (pixel size: 320x240) of non-mydriatic pupils (<3.5 mm pupil size) from 29 subjects. Manual annotations of pupil boundary and the center were marked by an expert grader. The dataset is divided into training (534 images from 18 subjects) and testing sets (120 images from 11 subjects).
Fig 1 shows the flowchart of the proposed pupil detection algorithm. The algorithm consists of two blocks: 1) coarse region-of-interest (ROI) finder and 2) fine-tuned pupil detector. Coarse ROI finder consisted of a single-shot detector (SSD) with 7 convolutional neural networks (CNN). A bounding box with the highest confidence score is used as the starting point for the fine-tuned detection. The Shootingstar algorithm is an extension of the Starburst pupil detection algorithm. The Shootingstar implementation shoots rays at five positions (the center and on the four corners of the bounding box). The Euclidean distances between the pupil centers determined by the algorithm in the test set were compared with the manual annotations.

Results : Fig 2 shows the results of correct and incorrect detections from the proposed algorithm. The algorithm achieved an accuracy of 91.5% in tracking pupils within 400 micrometers. Incorrect results are usually caused by the patient blinking or being in the middle of a blink. The execution time of the algorithm is 57.4 ± 3.8 ms using an Intel® Core™ i7-6920HQ CPU@2.90GHz.

Conclusions : The proposed algorithm provides a reliable solution for pupil detection for alignment guidance for fundus image capture. The algorithm can detect up to 17 frames per second and would be suitable for real-time pupil tracking.

This abstract was presented at the 2019 ARVO Imaging in the Eye Conference, held in Vancouver, Canada, April 26-27, 2019.

 

Fig 1. Workflow of the proposed pupil tracking algorithm

 

Fig 2. Top row shows correct detection; Bottom row shows incorrect detections. (Red = Manually annotated pupil, Blue = Algorithm detected center)