Seg-Cat: Automatic Pupil Detection from  Healthy and Cataract Patients

Author(s)	Ms. Sritha Uppaluru, Mr. Satkrit Yamarthy, Mr. Vivaan Rungta, Mr. Naksh Aggarwal, Mr. Arth Mishra
Country	India
Abstract	Cataract is among the leading causes of vision impairment worldwide, affecting approximately 94 million individuals who often lack access to timely diagnostic services. Early detection is critical to preventing irreversible vision loss. This paper presents Seg-Cat, a pupil segmentation pipeline designed to address a key limitation of current State-of-the-Art (SOTA) models: the tendency to focus on irrelevant visual features such as skin colour and eyebrow texture rather than the medically relevant pupil region. Seg-Cat employs classical image processing techniques alongside deep learning segmentation using a U-Net architecture, with novel shape-aware loss functions to enforce anatomically consistent pupil boundaries. Experimental results demonstrate that incorporating geometric regularisers (circular and elliptical loss) into the U-Net training objective yields significant improvements over the baseline, achieving a Test Dice score of 0.931 and IoU of 0.871 with the circular variant.
Keywords	Cataract Detection, Pupil Segmentation, U-Net, Circular Loss, Elliptical Loss, Hough Circle Detection, Medical Image Segmentation
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-10-18

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