Merge pull request #4 from ivadomed/nk/update_readme

brief update to readme

README.md

@@ -1,63 +1,50 @@
-# SCISeg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury
-
-[![medRxiv](https://img.shields.io/badge/medRxiv-10.1101/2024.01.03.24300794v2-blue.svg)](https://www.medrxiv.org/content/10.1101/2024.01.03.24300794v2.full.pdf)
-
-This repository contains the code for deep learning-based segmentation of the spinal cord and hyperintense lesions in spinal cord injury (SCI). The code is based on the [nnUNetv2 framework](https://github.com/MIC-DKFZ/nnUNet).
+# Assessing the Impact of Spinal Cord Curvature in Axial T2-weighted Intramedullary MS Lesion Segmentation
 
+This repository contains the code for deep learning-based segmentation of the spinal cord and intramedullary MS lesions in Axial T2-weighted MRI scans. The model is based on the [nnUNetv2 framework](https://github.com/MIC-DKFZ/nnUNet). This project is a collaboration between NeuroPoly (Polytechnique Montreal, Quebec) and TUM (Munich, Bavaria)
 
 ## Model Overview
 
-The model was trained on raw T2-weighted images of SCI patients from multiple (three) sites. The data included images with both axial and sagittal resolutions. To ensure uniformity across sites, all images were initially re-oriented to RPI. Given an input image, the model is able to segment *both* the lesion and the spinal cord. The model also works well on degenerative cervical myelopathy (DCM) lesions. 
+The model was trained on raw T2-weighted axial images of MS patients from multiple (four) sites. The TUM dataset is longitudinal (two sessions) and consisted of individual chunks (cervical, thoracic and lumbar) covering the entire spine. The three other sites used in this study were taken from the private `sct-testing-large` dataset from NeuroPoly. To ensure uniformity across sites, all images were initially re-oriented to RPI. Given an input image, the model is able to segment *both* the lesion and the spinal cord.
 
-<img width="1000" alt="figure2_fixed" src="https://github.com/ivadomed/model_seg_sci/assets/53445351/e7492462-18aa-4f7d-a03e-22863efaff72">
+TODO: add a figure here
 
-## Using SCIseg
+## Using the model
 
 ### Install dependencies
 
 - [Spinal Cord Toolbox (SCT) v6.2](https://github.com/spinalcordtoolbox/spinalcordtoolbox/releases/tag/6.2) or higher -- follow the installation instructions [here](https://github.com/spinalcordtoolbox/spinalcordtoolbox?tab=readme-ov-file#installation)
 - [conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) 
 - Python (v3.9)
 
-Once the dependencies are installed, download the latest SCIseg model:
+Once the dependencies are installed, download the latest model:
 
 ```bash
-sct_deepseg -install-task seg_sc_lesion_t2w_sci
+sct_deepseg -install-task seg_sc_lesion_t2w_ms
 ```
 
 ### Getting the lesion and spinal cord segmentation
 
 To segment a single image, run the following command: 
 
 ```bash
-sct_deepseg -i <INPUT> -task seg_sc_lesion_t2w_sci
+sct_deepseg -i <INPUT> -task seg_sc_lesion_t2w_ms
 ```
 
 For example:
 
 ```bash
-sct_deepseg -i sub-001_T2w.nii.gz -task seg_sc_lesion_t2w_sci
+sct_deepseg -i sub-001_T2w.nii.gz -task seg_sc_lesion_t2w_ms
 ```
 
 The outputs will be saved in the same directory as the input image, with the suffix `_lesion_seg.nii.gz` for the lesion 
 and `_sc_seg.nii.gz` for the spinal cord.
 
+## Analysis Pipeline
+
+TODO: 
+
 ## Citation Info
 
 If you find this work and/or code useful for your research, please cite our paper:
 
-```
-@article {Naga Karthik2024.01.03.24300794,
-	author = {Enamundram Naga Karthik* and Jan Valosek* and Andrew C. Smith and Dario Pfyffer and Simon Schading-Sassenhausen and Lynn Farner and Kenneth A. Weber II and Patrick Freund and Julien Cohen-Adad},
-	title = {SCIseg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury},
-	elocation-id = {2024.01.03.24300794},
-	year = {2024},
-	doi = {10.1101/2024.01.03.24300794},
-	publisher = {Cold Spring Harbor Laboratory Press},
-	URL = {https://www.medrxiv.org/content/early/2024/04/21/2024.01.03.24300794},
-	eprint = {https://www.medrxiv.org/content/early/2024/04/21/2024.01.03.24300794.full.pdf},
-	journal = {medRxiv},
-	note = {*Shared first authorship}
-}
-
-```
+TODO: