ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2023
Tensor-based Feature Extraction for Pupil Recognition in Cataract Surgery.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Identifying Biomarkers Using a Portable, Home-Based Eye-Tracking System to Predict Short-Term Visual Fatigue Deterioration: Prospective Observational Feasibility Study.JMIR human factors · 2026Observational
- Adaptive Tensor-Based Feature Extraction for Pupil Segmentation in Cataract Surgery.IEEE journal of biomedical and health informatics · 2024Article
- Evaluation of Methods for Detection and Semantic Segmentation of the Anterior Capsulotomy in Cataract Surgery Video.Clinical ophthalmology (Auckland, N.Z.) · 2024Article
- CatSkill: Artificial Intelligence-Based Metrics for the Assessment of Surgical Skill Level from Intraoperative Cataract Surgery Video Recordings.Ophthalmology scienceArticle
- A Computational Framework for Intraoperative Pupil Analysis in Cataract Surgery.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Cataract surgery remains the definitive treatment for cataracts, which are a major cause of preventable blindness worldwide. Adequate and stable dilation of the pupil are necessary for the successful performance of cataract surgery. Pupillary instability is a known risk factor for cataract surgery complications, and the accurate segmentation of the pupil from surgical video streams can enable the analysis of intraoperative pupil changes in cataract surgery. However, pupil segmentation performance can suffer due to variations in surgical illumination, obscuration of the pupil with surgical instruments, and hydration of the lens material intraoperatively. To overcome these challenges, we present a novel method called tensor-based pupil feature extraction (TPFE) to improve the accuracy of pupil recognition systems. We analyzed the efficacy of this approach with experiments performed on a dataset of 4,560 intraoperative annotated images from 190 cataract surgeries in human patients. Our results indicate that TPFE can identify features relevant to pupil segmentation and that pupil segmentation with state-of-the-art deep learning models can be significantly improved with the TPFE method.
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Registered trials
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