Automatic pupil detection using off-axis iris images for alignment guidance in fundus cameras
Bibliographic record
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 CLARUSTM 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".