Update README.md

README.md

@@ -1,11 +1,12 @@
 # BoneEnhance
 Methods for improving image quality on computed tomography -imaged bone
 
-(c) Santeri Rytky, University of Oulu, 2020-2021
+(c) Santeri Rytky, University of Oulu, 2021-2023
 
 ![Analysis pipeline](https://github.com/MIPT-Oulu/BoneEnhance/blob/master/images/Flowchart.png)
 
 ## Background
+Clinical cone-beam computed tomography (CBCT) devices are limited to imaging tissues of submillimeter scale. This repository is used to create super-resolution models trained on high-resolution micro-computed tomography (µCT) images. For a detailed description of the method, refer to the publication by Rytky SJO et al.
 
 ## Prerequisites
 - [Anaconda installation](https://docs.anaconda.com/anaconda/install/) 
@@ -19,13 +20,11 @@ conda env create -f environment.yml
 
 ### Model training
 
-- Create a training dataset: input images in folder `images` and target masks in `masks`. 
-For 2D data, just add the images to the corresponding folders, making sure that the image and mask names match.
-For 3D data, create a subfolder for each scan (sample name for the folder), and include the slices in the subfolder.
+- Create a training dataset: Use the script `create_training_data.py` to simulate image pairs from high-resolution µCT scans. Set the data and save paths as well as resolution and save parameters at the beginning of the script. 
 
 - Set the path name for training data in [session.py](../master/rabbitccs/training/session.py) (`init_experiment()` function)
 
-- Create a configuration file to the `experiments/run` folder. Four example experiments are included. 
+- Create a configuration file to the `experiments/run` folder. Example experiments are included in the folder. 
 All experiments are conducted subsequently during training.
 
 ```
@@ -36,9 +35,9 @@ python scripts/train.py
 ### Inference
 
 For 2D prediction, use `inference_tiles_2d.py`. For 3D data, use `inference_tiles_3d.py`. 
-Running `inference_tiles_large_3d.py` allows to run inference on larger samples, and merge on CPU.
+Running `inference_tiles_large_3d.py` allows to run inference on larger samples, and merge on CPU. Using `inference_tiles_large_pseudo3d.py` allows merging 2D predictions on orthogonal planes.
 Update the `snap` variable, image path and save directory.
 
 ## License
 
-This software is distributed under the MIT License.
+This software is distributed under the MIT License.