From ffc5e4f4f9ad320587afb21bfecfecaf3bc6f44a Mon Sep 17 00:00:00 2001 From: Theodore Pissas Date: Mon, 20 Jul 2020 04:52:19 +0100 Subject: [PATCH] fix README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 7719eeb..2c93fd2 100644 --- a/README.md +++ b/README.md @@ -11,17 +11,17 @@ This repository contains a Python3 Tensorflow implementation of the methods desc - [vgg19 pretrained weights][vgg19] (required only for computing a perceptual loss, must be downloaded and extracted) ## Contents -* Pretrained model weights and code to perform vessel segmentation on OCTA images. Specifically given a directory of OCT-A images, - it loads a pretrained model to segment them and saves the results. +* Pretrained model weights and code to perform vessel segmentation on OCTA images. * Demonstration of results on unseen publicly available OCT-A images including montages of multiple images and images captured with different commercial scanners. * Demonstration of the iterative refinement effect achieved by iUNET on unseen images (please see 'segmentation_iunet' directory) * Code for defining all models and loss functions and training as described in the paper. ## Usages-examples -* A commented example of how to use the pretrained models is provided in 'example_use_pretrained.py'. -* An example of how to define and train the models is provided in 'example_use_train.py'. -* Loss functions and network definitions can be found in 'losses.py' and 'networks.py' respectively. The 3 architectures that are implemented are -(a) UNET, (b) Stacked Hourglass Network and (c) iterative UNET or iUNET. +* A commented example of how to use the pretrained models is provided in 'example_use_pretrained.py' : specifically given a directory of OCT-A images, + it loads a pretrained model to segment them and saves the results. +* An example of how to define and train the models is provided in 'example_use_train.py'. +* Loss functions and network definitions can be found in 'losses.py' and 'networks.py' respectively. +The 3 architectures that are implemented are (a) UNET, (b) Stacked Hourglass Network and (c) iterative UNET or iUNET. ![fig](misc/figs/fig1.PNG )