Updated dataset installation instructions and updated IBSI 2 progress

README.md

@@ -19,7 +19,7 @@
   * [5. Tutorials](#5-tutorials)
   * [6. IBSI Standardization](#6-ibsi-standardization)
     * [IBSI Chapter 1](#ibsi-chapter-1)
-    * [IBSI Chapter 2 (In progress)](#ibsi-chapter-2-in-progress)
+    * [IBSI Chapter 2](#ibsi-chapter-2)
   * [7. Acknowledgement](#7-acknowledgement)
   * [8. Authors](#8-authors)
   * [9. Statement](#9-statement)
@@ -110,8 +110,8 @@ The image biomarker standardization initiative ([IBSI](https://theibsi.github.io
       - **Phase 1**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/ibsi/ibsi1p1.ipynb)
       - **Phase 2**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/ibsi/ibsi1p2.ipynb)
 
-  - ### IBSI Chapter 2 (In progress)
-      [The IBSI chapter 2](https://theibsi.github.io/ibsi2/) was launched in June 2020 and is still in progress. It is dedicated to the standardization of commonly used imaging filters in radiomic studies. We have created two [jupyter notebooks](https://github.com/MahdiAll99/MEDimage/tree/main/notebooks/ibsi) for each phase of the chapter and made them available for the users to run the IBSI tests for themselves and validate image filtering and image biomarker calculations from filter response maps. The tests can also be explored in interactive Colab notebooks that are directly accessible here: 
+  - ### IBSI Chapter 2
+      [The IBSI chapter 2](https://theibsi.github.io/ibsi2/) was launched in June 2020 and reached completion in February 2024. It is dedicated to the standardization of commonly used imaging filters in radiomic studies. We have created two [jupyter notebooks](https://github.com/MahdiAll99/MEDimage/tree/main/notebooks/ibsi) for each phase of the chapter and made them available for the users to run the IBSI tests for themselves and validate image filtering and image biomarker calculations from filter response maps. The tests can also be explored in interactive Colab notebooks that are directly accessible here: 
 
       - **Phase 1**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/ibsi/ibsi2p1.ipynb)
       - **Phase 2**: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MahdiAll99/MEDimage/blob/main/notebooks/ibsi/ibsi2p2.ipynb)
@@ -176,4 +176,4 @@ Here's what the license entails:
 9. The software author or license can not be held liable for any damages inflicted by the software.
 ```
 
-More information on about the [LICENSE can be found here](https://github.com/MahdiAll99/MEDimage/blob/main/LICENSE.md)
+More information on about the [LICENSE can be found here](https://github.com/MEDomics-UdeS/MEDimage/blob/main/LICENSE.md)

docs/tutorials.rst

@@ -3,18 +3,18 @@ Instructions
 
 Download dataset
 ----------------
-    In all tutorials, we use an open-access dataset containing medical images for various cancer types (Glioma, sarcoma...) 
-    and different imaging modalities (MR, CT, and PET). The dataset has been pre-processed to adhere to package norms.
+    In all tutorials, we use open-access data of medical images of various cancer types (Glioma, sarcoma...) 
+    and with different imaging modalities (MR, CT, and PET). All data has been pre-processed to adhere to package norms.
 
-    To download the dataset (~3.2 GB) and organize it in your local workspace, run the following command in your terminal from 
-    the package parent folder ::
+    In order to run the tutorials, you must first download the dataset. We recommend downloading only a subset (~347 MB) instead of the 
+    full dataset (~3.2 GB). To do so, run the following command in your terminal from the package parent folder: ::
     
-       python scripts/download_data.py --full-sts
+       python scripts/download_data.py --subset
     
     .. note::
-        The dataset is large, and options are available to download only a subset. For more information, run:
-        
-           python scripts/download_data.py --help
+        To download the full dataset, simply run the following command in your terminal from the package parent folder: ::
+
+           python scripts/download_data.py --full-sts
 
 CSV file
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