From 8ab505ec17f579e9daedef049bbe9b4702649de0 Mon Sep 17 00:00:00 2001 From: gurayerus Date: Thu, 12 Dec 2024 11:16:39 -0500 Subject: [PATCH 1/3] Edit doc --- README.md | 45 ++++------------------------ docs2/.installation.rst.kate-swp | Bin 0 -> 562 bytes docs2/.usage.rst.kate-swp | Bin 0 -> 1720 bytes docs2/installation.rst | 50 ++++--------------------------- 4 files changed, 11 insertions(+), 84 deletions(-) create mode 100644 docs2/.installation.rst.kate-swp create mode 100644 docs2/.usage.rst.kate-swp diff --git a/README.md b/README.md index c358c8ee..ea1096ef 100644 --- a/README.md +++ b/README.md @@ -1,46 +1,11 @@ # NiChart: Neuro-imaging Chart -NiChart is a comprehensive framework designed to revolutionize neuroimaging research. It offers large-scale neuroimaging capabilities, sophisticated analysis methods, and user-friendly tools, all seamlessly integrated into a local installation version and the [AWS Cloud](https://neuroimagingchart.com/portal/). +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. -## 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. 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 snakemake - mamba activate NCP_env - ``` -2. Manual installation - ```bash - git clone https://github.com/CBICA/NiChart_Project.git - pip install -r requirements.txt - ``` - -## Run NiChart Locally (GUI) -```bash -cd src/viewer/ -streamlit run NiChartProject.py -``` -The app will start in your localhost. +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 identical between the cloud and desktop applications. ## Quick Links diff --git a/docs2/.installation.rst.kate-swp b/docs2/.installation.rst.kate-swp new file mode 100644 index 0000000000000000000000000000000000000000..a3fcdf6663039c18288167202f7f3adad75cb88a GIT binary patch literal 562 zcmZ9|%MQUn6b9g_y2nbatR^0y5gQTrQbl(fE31YsBy|ZAOAGJPr|=LS$@#03Ge}1I z^~{{}cZ3jCFSv!$zXxYI+ee{nSxq5CZ4)<^!hU+L$W-1kc3<_*!(UM)OA9}9L4OLy z+VPbQdT(6W88~E{@P&;Y`u-d&xt@o&>;fFHi|~M!g%T>bxeRvPnn)l)K9&mU7pE%4AKCnlB a9>ZEc8&2;H{U-2^&rjj>9#MB_&7BX|Y&P`( literal 0 HcmV?d00001 diff --git a/docs2/.usage.rst.kate-swp b/docs2/.usage.rst.kate-swp new file mode 100644 index 0000000000000000000000000000000000000000..d4aafe03b7af6f42f452d9bea05d70b497c31fb1 GIT binary patch literal 1720 zcmeH{TS~)F5QfiD8+-9utJT&^ywzHv6gMEE5sKi0q3CPSh{d9%R$PHAa0~82pWKBT za070@^G{BgG3bNu4Wv0==I@#R3^P$m<&T4Y==zUA*F9=?LU*^`P)ZeYmG@@$yj6bt ztQG5DuTLMk(eqDDBxP<~FMttRD)rnl3kN?b&$kaL&NLCSVtwfOF>CWqhs;CZd*)&A z4KotzJ~Q^zcgzmB>ycAvN6nr76`>T-1a6xst`dyKpbw%%U^3oeCgau!;$-}R<7E6Y za#A&M70AKKVOJH&SGFbRq z1dIPKfrX!CusHt;SnTJ5g}>E=ehn=4Uk9JCzYVac=O);GHc_7n_?q=wV3EHH7WwH@ KEBvB+P5y5h-jp-| literal 0 HcmV?d00001 diff --git a/docs2/installation.rst b/docs2/installation.rst index 82b9555b..5ba6224a 100644 --- a/docs2/installation.rst +++ b/docs2/installation.rst @@ -2,54 +2,16 @@ Installation ############ -There are many ways you can install our package, using pip to download our latest stable version, -Docker, Singularity/Apptainer or manual installation directly from the source code. We highly suggest to install -our latest PyPI wheel. Note that the Singularity/Apptainer versions are outdated. +To install **NiChart Project** with pip, just do: :: + $ pip install NiChart_Project -**************** -Install with pip -**************** - -To install **NiChart DLMUSE** with pip, just do: :: - - $ pip install NiChart_DLMUSE - -We always have our latest stable version on PyPI, so we highly suggest you to install it this way, as this package is under -heavy development and building from source can lead to crashes and bugs. - - -.. _`Docker Container`: - -**************** -Docker Container -**************** - -The package comes already pre-built as a docker container that you can download at our `docker hub `_. -You can build the package by running the following command: :: - - $ docker build -t cbica/nichart_dlmuse . - -.. _`Singularity/Apptainer build` - -Singularity and Apptainer images can be built for NiChart_DLMUSE, allowing for frozen versions of the pipeline and easier -installation for end-users. Note that the Singularity project recently underwent a rename to "Apptainer", with a commercial -fork still existing under the name "Singularity" (confusing!). Please note that while for now these two versions are largely identical, -future versions may diverge. It is recommended to use the AppTainer distribution. For now, these instructions apply to either. -After installing the container engine, run: :: - - $ singularity build nichart_dlmuse.sif singularity.def - -This will take some time, but will build a containerized version of your current repo. Be aware that this includes any local changes! -The nichart_dlmuse.sif file can be distributed via direct download, or pushed to a container registry that accepts SIF images. - -.. _`Manual installation` +We always have our latest stable version on PyPI, so we highly suggest you to install it this way. You can manually build the package from source by running: :: - $ git clone https://github.com/CBICA/NiChart_DLMUSE + $ git clone https://github.com/CBICA/NiChart_Project - $ cd NiChart_DLMUSE && python3 -m pip install -e . + $ cd NiChart_Project && python3 -m pip install -e . -We **do not** recomment installing the package directly from source as the repository above is under heavy development and can cause -crashes and bugs. +We **do not** recomment installing the package directly from source as the repository is under development and this can cause crashes and bugs. From 92ebecaf299561a529206773de666b89110e2f51 Mon Sep 17 00:00:00 2001 From: gurayerus Date: Thu, 12 Dec 2024 13:25:28 -0500 Subject: [PATCH 2/3] Edit README --- README.md | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) diff --git a/README.md b/README.md index ea1096ef..592e3780 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,59 @@ reference distributions for comparison to a broader population. 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 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. + ## Quick Links [![NiChart Website & Cloud](https://img.shields.io/badge/-Website-blue?style=for-the-badge&logo=world&logoColor=white)](https://neuroimagingchart.com/) [![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white)](https://hub.docker.com/u/cbica) [![AIBIL Research](https://img.shields.io/badge/-Research-blue?style=for-the-badge&logo=google-scholar&logoColor=white)](https://aibil.med.upenn.edu/research/) [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/@NiChart-UPenn) From fb795dc4a9033754346f5e825e6ba75e11da2d1a Mon Sep 17 00:00:00 2001 From: gurayerus Date: Thu, 12 Dec 2024 13:42:56 -0500 Subject: [PATCH 3/3] Edit doc --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 592e3780..a3c5b735 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ AI biomarkers, allowing to map a subject’s MRI images into personalized measur reference distributions for comparison to a broader population. 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 identical between the cloud and desktop applications. +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