bids_nichart_dlmuse

https://github.com/cbica/bids_nichart_dlmuse

Science Score: 52.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
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Academic email domains
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Institutional organization owner
Organization cbica has institutional domain (www.med.upenn.edu)
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (7.2%) to scientific vocabulary

Last synced: 7 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: CBICA
License: bsd-3-clause
Language: Python
Default Branch: main
Size: 4.1 MB

Statistics

Stars: 0
Watchers: 0
Forks: 0
Open Issues: 1
Releases: 1

Created about 1 year ago · Last pushed 7 months ago

Metadata Files

Readme Changelog Contributing License Citation

README.rst

############################################################
*BIDS_NiChart_DLMUSE*: A BIDS-App wrapper for NiChart DLMUSE
############################################################

*****
About
*****

This is a BIDS-App wrapper for `NiChart DLMUSE `_, a tool for brain mask extraction, brain segmentation and getting ROI volumes.

Installation
------------

BIDS_NiChart_DLMUSE can be installed using pip:

.. code-block:: bash

    pip install ncdlmuse==

Or using Docker:

.. code-block:: bash

    docker pull cbica/ncdlmuse:

Or using Singularity:

.. code-block:: bash

    singularity build ncdlmuse.sif docker://cbica/ncdlmuse:

Usage
-----

Basic usage:

.. code-block:: bash

    # participant-level analysis
    # if no `--participant-label` is provided, run all
    ncdlmuse bids_dir output_dir participant --device cuda

    # group-level analysis
    ncdlmuse bids_dir output_dir group

For more options:

.. code-block:: bash

    ncdlmuse --help

More information and documentation can be found at https://cbica.github.io/NiChart_DLMUSE.

Using Singularity (Recommended):
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: bash

    # participant-level analysis
    singularity run --nv --cleanenv \
           -B /path/to/bids/dir:/data:ro \
           -B /path/to/output/dir:/out \
           -B /path/to/work/dir:/work \
           ncdlmuse.sif \
                /data \
                /out \
                participant \
                --participant-label 01 \
                --session-id 1 \
                --device cuda \
                --work-dir /work \
                --stop-on-first-crash \
                --skip_bids_validation

    # group-level analysis
    singularity run --nv --cleanenv \
           -B /path/to/bids/dir:/data:ro \
           -B /path/to/output/dir:/out \
           -B /path/to/work/dir:/work \
           ncdlmuse.sif \
                /data \
                /out \
                group

Owner

Name: Center for Biomedical Image Computing & Analytics (CBICA)
Login: CBICA
Kind: organization
Email: software@cbica.upenn.edu
Location: Philadelphia, PA

Website: https://www.med.upenn.edu/cbica/
Twitter: CBICAannounce
Repositories: 21
Profile: https://github.com/CBICA

CBICA focuses on the development and application of advanced computation techniques.

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  BIDS_NiChart_DLMUSE: A BIDS-app wrapper for NiChart DLMUSE
message: >-
  If you use this software, please cite
  (doi: 10.1016/j.neuroimage.2015.11.073).
type: software
authors:
  - given-names:
    family-names:
    email:
    affiliation:
    orcid:
identifiers:
  - type:
    value:
    description:
repository-code: 'https://github.com/CBICA/BIDS_NiChart_DLMUSE'
url: 'https://cbica.github.io/NiChart_DLMUSE'
abstract: >-
  NiChart_DLMUSE is a package that allows the users to process their brain imaging (sMRI) 
  data easily and efficiently.

  NiChart_DLMUSE offers easy ICV (Intra-Cranial Volume) mask extraction, and brain 
  segmentation into ROIs. 
  This is achieved through the DLICV (https://github.com/CBICA/DLICV) and DLMUSE (https://github.com/CBICA/DLMUSE) methods. 
  Intermediate step results are saved for easy access to the user.
keywords:
  - DLMUSE
  - DLICV
  - BIDS-App
  - Neuroimaging
license: BSD-3-Clause
version: 0.7.5
date-released: '2025-05-28'

GitHub Events

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Issues event: 1
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Issue comment event: 8
Member event: 3
Push event: 98
Pull request review event: 2
Pull request event: 24
Create event: 19

Last Year

Issues event: 1
Delete event: 14
Issue comment event: 8
Member event: 3
Push event: 98
Pull request review event: 2
Pull request event: 24
Create event: 19

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 1
Total pull requests: 14
Average time to close issues: N/A
Average time to close pull requests: 28 days
Total issue authors: 1
Total pull request authors: 2
Average comments per issue: 0.0
Average comments per pull request: 0.71
Merged pull requests: 8
Bot issues: 0
Bot pull requests: 9

Past Year

Issues: 1
Pull requests: 14
Average time to close issues: N/A
Average time to close pull requests: 28 days
Issue authors: 1
Pull request authors: 2
Average comments per issue: 0.0
Average comments per pull request: 0.71
Merged pull requests: 8
Bot issues: 0
Bot pull requests: 9

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Dependencies

.github/workflows/lint.yml actions

actions/checkout v4 composite
codespell-project/actions-codespell v2 composite

Dockerfile docker

pytorch/pytorch ${TORCH_VERSION}-cuda${CUDA_VERSION}-cudnn${CUDNN_VERSION}-runtime build

.maint/requirements.txt pypi

click *
fuzzywuzzy *

pyproject.toml pypi

acres *
fmriprep *
huggingface_hub *
importlib_resources python_version < "3.11"
indexed_gzip <= 1.9.4
looseversion *
networkx == 2.8.8
nibabel <= 5.3.2
nilearn ~= 0.11.0
nipype >= 1.8.5
niworkflows ~= 1.13.0
numpy >= 1.26
packaging *
pandas *
psutil <= 7.0.0
pybids <= 0.19.0
requests *
scipy *
templateflow <= 24.2.2
toml *
torch ==2.3.1

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