# DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images

arXiv: https://arxiv.org/abs/1803.00232 · HTML: https://mlsanity.com/paper/1803.00232/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [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)
- [Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review](https://mlsanity.com/paper/2109.02322/) (2021)
- [AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing](https://mlsanity.com/paper/2206.04689/) (2022)
- [Elastic deformation of optical coherence tomography images of diabetic macular edema for deep-learning models training: how far to go?](https://mlsanity.com/paper/2107.03651/) (2021)
- [Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography](https://mlsanity.com/paper/2210.06664/) (2022)

## Abstract (arXiv)

Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm was designed and trained to digitally stain (i.e. highlight) 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall dice coefficient (mean of all tissues) was 0.91 ± 0.05 when assessed against manual segmentations performed by an expert observer. We offer here a robust segmentation framework that could be extended for the automated parametric study of the ONH tissues.