Merge pull request #58 from CBICA/ge-dev

Ge dev docs changes to deploy

README.md

@@ -6,61 +6,29 @@ reference distributions for comparison to a broader population.
 
 ![NiChart flowchart](resources/images/NiChart_Flowchart_v2.svg)
 
-We provide both locally deployable software and a cloud application. [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 completely identical between the cloud and desktop applications.
-
-## Components
-
-1. **Image Processing**: Utilizes tools like [DLMUSE](https://github.com/CBICA/NiChart_DLMUSE), [fMRIPrep](https://github.com/nipreps/fmriprep) [XCEPengine](https://github.com/PennLINC/xcp_d), and [QSIPrep](https://github.com/PennLINC/qsiprep) for effective image analytics.
-2. **Reference Data Curation**: Houses ISTAGING, 70000 Scans, and 14 individual studies to provide curated reference data.
-3. **Data Harmonization**: Employs [neuroharmonize](https://github.com/rpomponio/neuroHarmonize) and [Combat](https://github.com/Zheng206/ComBatFam_Pipeline) for ensuring consistent data standards.
-4. **Machine Learning Models**: Provides Supervised, Semi-supervised, and DL Models for advanced neuroimaging analysis including [SpareScore](https://github.com/CBICA/spare_score).
-5. **Data Visualization**: Features like Centile curves, direct image linking, and reference values for comprehensive data visualization.
-6. **Deployment**: Supports open-source Github components and Docker container compatibility deployed in a local environment & [AWS Cloud](https://aws.amazon.com/).
-
-
-## System Requirements
-
-For recommended system configuration, please refer to: [nnUNet hardware requirements](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/installation_instructions.md#hardware-requirements).
-
-## Installation Instructions
-
-1. (Optional but recommended for environment management) Mamba installation
-    [Mamba Installation Guide (Official)](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)
-
-    Example (Linux x86):
-    ```bash
-    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
-    ```
-2. Install NiChart_Project into the environment
-   ```bash
-   git clone https://github.com/CBICA/NiChart_Project.git
-   pip install -r requirements.txt
-   ```
-
-3. Install the proper PyTorch version for your device
-   Numpy and PyTorch have some compatibility issues which have changed variously on different platforms. To avoid frustration with these issues, please install PyTorch as noted below.
-
-   After installing all other requirements, uninstall Torch:
-   ```
-   pip uninstall torch
-   ```
-
-   Then install PyTorch using the following command. Make sure to use the correct index url for your CUDA version as specified on the [PyTorch getting started page](https://pytorch.org/get-started/locally/).
-   On Linux, use version 2.3.1. On Windows, 2.5.1 is known to work.
-   ```
-   pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu121
-   ```
-## Run NiChart Locally (GUI)
-```bash
-cd src/viewer/
-streamlit run NiChartProject.py
-```
-The app will start in your localhost.
+## 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. Enabling the **continuous integration of cutting-edge processing and analysis methods** to extract meaningful AI biomarkers from multi-modal 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 result **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 our [documentation](https://cbica.github.io/NiChart_Project/) and [our GitHub page](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
 

README.rst

@@ -0,0 +1,53 @@
+NiChart: Neuro-imaging Chart
+============================
+
+Full documentation at https://cbica.github.io/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 our documentation (https://cbica.github.io/NiChart_Project) and our GitHub page (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
+-----------
+
+`NiChart Website & Cloud <https://neuroimagingchart.com>` 
+
+`AIBIL Research <https://aibil.med.upenn.edu/research>`
+
+`YouTube <https://www.youtube.com/@NiChart-UPenn>`
+
+`Twitter <https://x.com/NiChart_AIBIL>`
+
+© 2024 CBICA. All Rights Reserved.

docs2/README.rst

@@ -0,0 +1,53 @@
+NiChart: Neuro-imaging Chart
+============================
+
+Full documentation at https://cbica.github.io/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 [FIXME: link to readthedocs and Github]. 
+
+[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
+-----------
+
+`NiChart Website & Cloud <https://neuroimagingchart.com>` 
+
+`AIBIL Research <https://aibil.med.upenn.edu/research>`
+
+`YouTube <https://www.youtube.com/@NiChart-UPenn>`
+
+`Twitter <https://x.com/NiChart_AIBIL>`
+
+© 2024 CBICA. All Rights Reserved.

docs2/api.rst

@@ -1,70 +1,5 @@
-NiChart\_DLMUSE source code
+Developers - API
 ===========================
 
 Submodules
 ----------
-
-DLMUSE pipeline
---------------------------------------------
-
-.. automodule:: NiChart_DLMUSE.dlmuse_pipeline
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Calculate ROI Volume module
------------------------------------------
-
-.. automodule:: NiChart_DLMUSE.CalcROIVol
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-
-Mask Image
---------------------------------
-
-.. automodule:: NiChart_DLMUSE.MaskImage
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Relabel ROI
---------------------------------
-
-.. automodule:: NiChart_DLMUSE.RelabelROI
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Reorient Image
-------------------------------------------
-
-.. automodule:: NiChart_DLMUSE.ReorientImage
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Segment Image
----------------------------------------------
-
-.. automodule:: NiChart_DLMUSE.SegmentImage
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-util functions
-----------------------------
-
-.. automodule:: NiChart_DLMUSE.utils
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: NiChart_DLMUSE
-   :members:
-   :undoc-members:
-   :show-inheritance:

docs2/components.rst

@@ -0,0 +1,86 @@
+##########
+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
+   :target https://github.com/CBICA/NiChart_DLMUSE
+   :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
+  :target https://github.com/CBICA/spare_scores
+  :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

@@ -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

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