GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans

no code implementations • 28 Jun 2022 • Paria Jeihouni, Omid Dehzangi, Annahita Amireskandari, Ali Rezai, Nasser M. Nasrabadi

In this paper, we design a Generative Adversarial Network (GAN)-based solution for super-resolution and segmentation of optical coherence tomography (OCT) scans of the retinal layers.

Generative Adversarial Network Segmentation +1

Superresolution and Segmentation of OCT scans using Multi-Stage adversarial Guided Attention Training

no code implementations • 10 Jun 2022 • Paria Jeihouni, Omid Dehzangi, Annahita Amireskandari, Ali Dabouei, Ali Rezai, Nasser M. Nasrabadi

Our ablation study results on the WVU-OCT data-set in five-fold cross-validation (5-CV) suggest that the proposed MultiSDGAN with a serial attention module provides the most competitive performance, and guiding the spatial attention feature maps by binary masks further improves the performance in our proposed network.

Generative Adversarial Network SSIM

Efficient OCT Image Segmentation Using Neural Architecture Search

no code implementations • 28 Jul 2020 • Saba Heidari Gheshlaghi, Omid Dehzangi, Ali Dabouei, Annahita Amireskandari, Ali Rezai, Nasser M. Nasrabadi

We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and pre-processed OCT image dataset.

Image Segmentation Neural Architecture Search +3