Merge pull request #63 from CBICA/ge-dev

Ge dev

.github/workflows/sphinx-docs.yml

@@ -29,16 +29,16 @@ jobs:
           pip install sphinx sphinx_rtd_theme sphinx-tabs sphinx_toolbox
       - name: Sphinx build
         run: |
-          sphinx-apidoc -o docs2 src/
-          cd docs2/
+          sphinx-apidoc -o docs src/
+          cd docs/
           make html
           cd ..
 
       - name: Upload pages artifact
         uses: actions/upload-pages-artifact@v3
 
         with:
-          path: docs2/_build/html
+          path: docs/_build/html
           retention-days: 90
 
   deploy-docs:

README.rst

@@ -0,0 +1,64 @@
+NiChart: Neuro-imaging Chart
+============================
+
+.. image:: https://img.shields.io/badge/Documentation-Read_the_Docs-blue
+    :target: https://cbica.github.io/NiChart_Project
+
+.. image:: https://img.shields.io/badge/Website-NeuroImagingChart-orange
+    :target: https://neuroimagingchart.com
+
+.. image:: https://img.shields.io/badge/GitHub-CBICA/NiChart_Project-green
+    :target: https://github.com/CBICA/NiChart_Project
+
+About
+-----
+
+*NiChart* is a novel AI-powered neuroimaging platform with tools for computing a dimensional chart from multi-modal MRI data. *NiChart* provides end-to-end pipelines from raw DICOM data to advanced
+AI biomarkers, allowing to map a subject’s MRI images into personalized measurements, along with
+reference distributions for comparison to a broader population.
+
+.. image:: https://raw.githubusercontent.com/CBICA/NiChart_Project/refs/heads/ge-dev/resources/images/NiChart_Flowchart_v2.svg
+  :alt: NiChart Flowchart
+
+The Basics
+----------
+
+The development of nichart is guided by several core principles:
+
+ 1. Enabling **near real-time image processing and analysis** through advanced methods.
+
+ 2. Facilitating the **continuous integration** of **cutting-edge methods** for extracting novel **AI biomarkers** from neuroimaging data.
+
+ 3. Ensuring robust and reliable results through **extensive data training and validation** on large and diverse training datasets.
+
+ 4. Providing user-friendly tools for **visualization and reporting**.
+
+ 5. Developing a deployment strategy that enables **easy access** for users with varying technical expertise and hardware resources.
+
+Running NiChart
+---------------
+
+We provide both a locally installable **desktop application** and a **cloud-based application**. 
+
+For the desktop application please see `NiChart GitHub <https://github.com/CBICA/NiChart_Project>`_.
+
+`NiChart cloud application <https://neuroimagingchart.com/portal>`_, hosted via Amazon Web Services (AWS), deploys scalable infrastructure which hosts the *NiChart* tools as a standard web application accessible via the user’s web browser. 
+
+The cloud and desktop applications are unified at the code level through the use of the Python library `Streamlit <https://streamlit.io>`_. Consequently, the user experience is nearly identical between the cloud and desktop applications.
+
+Quick Links
+-----------
+
+.. image:: https://img.shields.io/badge/Research-AIBIL-blue
+    :target: https://aibil.med.upenn.edu/research
+    :alt: AIBIL Research
+
+.. image:: https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white
+    :target: https://www.youtube.com/@NiChart-UPenn
+    :alt: YouTube
+
+.. image:: https://img.shields.io/twitter/url/https/twitter.com/NiChart_AIBIL.svg?style=social&label=Follow%20%40NiChart_AIBIL
+    :target: https://x.com/NiChart_AIBIL
+    :alt: Twitter
+
+© 2024 CBICA. All Rights Reserved.

docs/Makefile

@@ -5,8 +5,8 @@
 # from the environment for the first two.
 SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
+SOURCEDIR     = .
+BUILDDIR      = _build
 
 # Put it first so that "make" without argument is like "make help".
 help:

docs/README.rst

@@ -0,0 +1,64 @@
+NiChart: Neuro-imaging Chart
+============================
+
+.. image:: https://img.shields.io/badge/Documentation-Read_the_Docs-blue
+    :target: https://cbica.github.io/NiChart_Project
+
+.. image:: https://img.shields.io/badge/Website-NeuroImagingChart-orange
+    :target: https://neuroimagingchart.com
+
+.. image:: https://img.shields.io/badge/GitHub-CBICA/NiChart_Project-green
+    :target: https://github.com/CBICA/NiChart_Project
+
+About
+-----
+
+*NiChart* is a novel AI-powered neuroimaging platform with tools for computing a dimensional chart from multi-modal MRI data. *NiChart* provides end-to-end pipelines from raw DICOM data to advanced
+AI biomarkers, allowing to map a subject’s MRI images into personalized measurements, along with
+reference distributions for comparison to a broader population.
+
+.. image:: https://raw.githubusercontent.com/CBICA/NiChart_Project/refs/heads/ge-dev/resources/images/NiChart_Flowchart_v2.svg
+  :alt: NiChart Flowchart
+
+The Basics
+----------
+
+The development of nichart is guided by several core principles:
+
+ 1. Enabling **near real-time image processing and analysis** through advanced methods.
+
+ 2. Facilitating the **continuous integration** of **cutting-edge methods** for extracting novel **AI biomarkers** from neuroimaging data.
+
+ 3. Ensuring robust and reliable results through **extensive data training and validation** on large and diverse training datasets.
+
+ 4. Providing user-friendly tools for **visualization and reporting**.
+
+ 5. Developing a deployment strategy that enables **easy access** for users with varying technical expertise and hardware resources.
+
+Running NiChart
+---------------
+
+We provide both a locally installable **desktop application** and a **cloud-based application**. 
+
+For the desktop application please see `NiChart GitHub <https://github.com/CBICA/NiChart_Project>`_.
+
+`NiChart cloud application <https://neuroimagingchart.com/portal>`_, hosted via Amazon Web Services (AWS), deploys scalable infrastructure which hosts the *NiChart* tools as a standard web application accessible via the user’s web browser. 
+
+The cloud and desktop applications are unified at the code level through the use of the Python library `Streamlit <https://streamlit.io>`_. Consequently, the user experience is nearly identical between the cloud and desktop applications.
+
+Quick Links
+-----------
+
+.. image:: https://img.shields.io/badge/Research-AIBIL-blue
+    :target: https://aibil.med.upenn.edu/research
+    :alt: AIBIL Research
+
+.. image:: https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white
+    :target: https://www.youtube.com/@NiChart-UPenn
+    :alt: YouTube
+
+.. image:: https://img.shields.io/twitter/url/https/twitter.com/NiChart_AIBIL.svg?style=social&label=Follow%20%40NiChart_AIBIL
+    :target: https://x.com/NiChart_AIBIL
+    :alt: Twitter
+
+© 2024 CBICA. All Rights Reserved.

docs/api.rst

@@ -0,0 +1,5 @@
+Developers - API
+===========================
+
+Submodules
+----------

docs/components.rst

@@ -0,0 +1,84 @@
+##########
+Components
+##########
+
+NiChart is designed to integrate independent image processing and analysis pipelines. The following sections detail these pipelines.
+
+*****************
+Current Pipelines
+*****************
+
+These pipelines are currently active and accessible within the NiChart Project
+
+====================================
+1. sMRI Biomarkers of Disease and Aging
+====================================
+
+Neuroimaging pipeline for computing AI biomarkers of disease and aging from T1-weighted MRI scans. The pipeline applies the following steps for processign and analysis.
+
+------------
+Segmentation
+------------
+
+`DLMUSE <https://neuroimagingchart.com/components/#Image%20Processing>`_: Rapid and accurate **brain anatomy segmentation**
+
+.. image:: https://github.com/CBICA/NiChart_Project/blob/031d1cafc5091eb514511ee25af189d5f0b5ac56/resources/images/dlicv%2Bdlmuse_segmask.png
+   :alt: DLMUSE
+
+-------------
+Harmonization
+-------------
+
+`COMBAT <https://neuroimagingchart.com/components/#Harmonization>`_: **Statistical data harmonization** of ROI volumes to `reference data <https://neuroimagingchart.com/components/#Reference%20Dataset>`_
+
+.. image:: https://raw.githubusercontent.com/CBICA/NiChart_Project/refs/heads/ge-dev/resources/images/combat_agetrend.png
+   :alt: COMBAT
+
+------------------------
+Supervised ML Biomarkers
+------------------------
+
+`SPARE-AD and SPARE-Age indices <https://neuroimagingchart.com/components/##Machine%20Learning%20Models>`_: AI biomarkers of **Alzheimer's Disease and Aging** related brain atrophy patterns
+
+.. image:: https://raw.githubusercontent.com/CBICA/NiChart_Project/refs/heads/ge-dev/resources/images/sparead%2Bage.png
+  :alt: SPARE-AD and SPARE-Age indices
+
+`SPARE-CVR indices <https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.067709>`_: AI biomarkers of brain atrophy patterns associated with **Cardio-Vascular Risk Factors**
+
+.. image:: https://raw.githubusercontent.com/CBICA/NiChart_Project/refs/heads/ge-dev/resources/images/sparecvr.png
+  :alt: SPARE-CVR indices, Govindarajan, S.T., et. al., Nature Communications, 2024
+
+-----------------------------
+Semi-supervised ML Biomarkers
+-----------------------------
+
+ - `SurrealGAN indices <https://www.nature.com/articles/d41586-024-02692-z>`_: Data-driven phenotyping of brain aging, **5 Brain Aging Subtypes**
+
+.. image:: https://raw.githubusercontent.com/CBICA/NiChart_Project/refs/heads/ge-dev/resources/images/sgan1.jpg
+   :alt: SurrrealGAN indices
+
+====================================
+2. WM Lesion Segmentation
+====================================
+
+Neuroimaging pipeline for segmenting white matter lesions on FLAIR MRI scans.
+
+`DLWMLS <https://neuroimagingchart.com/components/#Image%20Processing>`_: Rapid and accurate **white matter lesion segmentation**
+
+.. image:: https://github.com/CBICA/NiChart_Project/blob/031d1cafc5091eb514511ee25af189d5f0b5ac56/resources/images/dlwmls.png
+   :target https://github.com/CBICA/NiChart_DLWMLS
+   :alt: DLWMLS
+
+*****************
+Under Development
+*****************
+
+These pipelines are planned for integration in future NiChart releases.
+
+====================================
+1. DTI Biomarkers of Disease and Aging
+====================================
+
+====================================
+2. fMRI Biomarkers of Disease and Aging
+====================================

docs2/help.rst → docs/help.rst

@@ -2,7 +2,7 @@
 Getting help
 ############
 
-If you have a problem or would like to ask a question about the inner workings of **NiChart DLMUSE** you can directly
+If you have a question about **NiChart Project** you can directly
 message our maintaners via email.
 
 .. note::

docs2/index.rst → docs/index.rst

@@ -1,4 +1,4 @@
-.. include:: ../README.rst
+.. include:: README.rst
 
 ########
 Contents
@@ -9,6 +9,8 @@ Contents
 
    installation
    usage
+   components
    contributors
    api
    help
+   license

docs/installation.rst

@@ -0,0 +1,47 @@
+############
+Installation
+############
+
+Users can install **NiChart Project** with pip: ::
+
+    pip install NiChart_Project
+
+Alternatively, the package can be installed from source: ::
+
+    git clone https://github.com/CBICA/NiChart_Project
+    cd NiChart_Project && python3 -m pip install -e .
+
+We release our latest stable version on PyPI; accordingly, **we strongly recommend pip installation**.
+
+.. warning::
+    PyTorch and NumPy have known compatibility issues across different platforms. To avoid potential conflicts, please follow the installation instructions below.
+
+- After installing all other necessary packages, uninstall any existing Torch installations: ::
+
+   $ pip uninstall torch
+
+- Reinstall PyTorch:
+
+    - **Linux:** PyTorch version 2.3.1
+    - **Windows:** PyTorch version 2.5.1
+
+- Users can select the correct index URL for their CUDA version based on the `PyTorch getting started page <https://pytorch.org/get-started/locally>`_
+
+**Example on a Linux x86 system:** ::
+
+    $ pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu121
+
+*************************
+Managing your environment
+*************************
+
+We recommend installing NiChart Project within a dedicated environment. Users can create an environment using Mamba (please see `Mamba Installation Guide <https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html>`_).
+
+**Example on a Linux x86 system:** ::
+
+    $ wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh
+      bash Mambaforge-Linux-x86_64.sh
+      mamba create -c conda-forge -c bioconda -n NCP_env python=3.12
+      mamba activate NCP_env
+      git clone https://github.com/CBICA/NiChart_Project.git
+      pip install -r requirements.txt

docs/license.rst

@@ -0,0 +1,5 @@
+#######
+License
+#######
+
+NiChart application, web site, the content and the logo of NiChart are intellectual property of CBICA, UPENN. For details, please see `the licence <https://github.com/CBICA/NiChart_Project/blob/main/LICENSE>`_.