# A feature agnostic approach for glaucoma detection in OCT volumes

arXiv: https://arxiv.org/abs/1807.04855 · HTML: https://mlsanity.com/paper/1807.04855/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2018.

## Built on by (most cited first, 12 of 12)

- [OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics](https://mlsanity.com/paper/2209.11195/) (2022)
- [Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis](https://mlsanity.com/paper/2204.06931/) (2022)
- [Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks](https://mlsanity.com/paper/2106.13551/) (2021)
- [Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma](https://mlsanity.com/paper/2204.07004/) (2022)
- [AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing](https://mlsanity.com/paper/2206.04689/) (2022)
- [ETSCL: An Evidence Theory-Based Supervised Contrastive Learning Framework for Multi-modal Glaucoma Grading](https://mlsanity.com/paper/2407.14230/) (2024)
- [3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia -- A Key to Improving Glaucoma Diagnosis in Myopic Populations](https://mlsanity.com/paper/2503.19083/) (2025)
- [A self-training framework for glaucoma grading in OCT B-scans](https://mlsanity.com/paper/2111.11771/) (2021)
- [Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging](https://mlsanity.com/paper/2403.05702/) (2024)
- [AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review](https://mlsanity.com/paper/2410.15947/) (2024)
- [Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review](https://mlsanity.com/paper/2411.05876/) (2024)
- [AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification](https://mlsanity.com/paper/2510.00882/) (2025)

## Abstract (arXiv)

Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly used for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have utilized segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.