Science Score: 57.0%

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

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 3 DOI reference(s) in README
  • Academic publication links
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  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (14.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: onurunaall
  • License: other
  • Language: Python
  • Default Branch: refactor_general
  • Size: 57 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 9 months ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

AugmentA: Patient-specific Augmented Atrial model Generation Tool

We propose a patient-specific Augmented Atrial model Generation Tool (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA consists firstly of a pre-processing step applied to the input geometry. Secondly, the atrial orifices are identified and labelled using only one reference point per atrium. If the workflow includes fitting a statistical shape model (SSM) to the input geometry, this is first rigidly aligned with the given mean shape and finally a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation using a Laplace-Dirichlet-Rule-based-Method.

Pipeline

Files and Folders

  • main.py: AugmentA's main script
  • mesh/: contains the exemplary mesh and the statistical shape model
  • standalones/: standalone tools used in the pipeline
  • template/: template for non-rigid fitting process
  • Atrial_LDRBM/: Laplace-Dirichlet-Rule-based-Method to annotate anatomical regions and generate atrial fiber orientation in the atria

Setup

Create a python virtual environment to install the current requirements after installing the requirements of carputils:

python -m venv ~/myEnv source ~/myEnv/bin/activate pip install -r requirements.txt

Download the submodule with the helper methods: git submodule update --init --recursive

Install PyMesh

Go to the carputils folder and re-install carputils' requirements (assuming that carputils was installed in the home folder):

cd ~/carputils pip install -r requirements.txt

Usage

Remember to source to myEnv before using the pipeline:

source ~/myEnv/bin/activate

Show all options:

python main.py --help

Example using an MRI segmentation to produce a bilayer atrial model:

python main.py --mesh mesh/LA_MRI.vtp --closed_surface 0 --use_curvature_to_open 1 --atrium LA --open_orifices 1 --MRI 1

Example using a closed surface derived from a MRI segmentation to produce a volumetric atrial model:

python main.py --mesh mesh/mwk05_bi.vtp --closed_surface 1 --use_curvature_to_open 0 --atrium LA_RA

Q&A

  • Selection of appendage apex: the selected point will be used as boundary condition for a Laplacian problem. Therefore, the point at the center of the appendage is the most suitable to identify the whole appendage body
  • FiberLA: LAA labeling (check LPVs identification functions distinguishPvs and optimizePVs in lagenerate_fiber.py)
  • FiberRA: PMs (check step in function Method.downsamplepath in rageneratefiber.py), bridges (boolean operations and normal directions of original mesh)
  • If facing problems with PyMesh install it from https://github.com/PyMesh/PyMesh and follow the instructions

Citation

When using this work, please cite

AugmentA: Patient-specific Augmented Atrial model Generation Tool

Luca Azzolin, Martin Eichenlaub, Claudia Nagel, Deborah Nairn, Jorge Sánchez, Laura Unger, Olaf Dössel, Amir Jadidi, Axel Loewe doi:10.1101/2022.02.13.22270835

License

All source code is subject to the terms of the Academic Public License. Copyright 2021 Luca Azzolin, Karlsruhe Institute of Technology.

Contact

Luca Azzolin
Institute of Biomedical Engineering
Karlsruhe Institute of Technology
www.ibt.kit.edu

Owner

  • Name: Onur Ünal
  • Login: onurunaall
  • Kind: user
  • Location: Turkey

Citation (CITATION.cff)

cff-version: 1.2.0
title: "AugmentA: Patient-specific Augmented Atrial model Generation Tool"
message: "Please cite this software using the metadata from 'preferred-citation'."
type: software
authors:
  - given-names: Luca
    family-names: Azzolin
    email: luca.azzolin@kit.edu
    affiliation: Karlsruhe Institute of Technology (KIT)
    orcid: https://orcid.org/0000-0002-2919-4617
  - given-names: Axel
    family-names: Loewe
    email: axel.loewe@kit.edu
    affiliation: Karlsruhe Institute of Technology (KIT)
    orcid: https://orcid.org/0000-0002-2487-4744
license: APL-1.0
identifiers:
  - description: "Description of AugmentA"
    type: doi
    value: 10.1101/2022.02.13.22270835
repository-code: https://github.com/KIT-IBT/AugmentA
preferred-citation:
  type: article
  title: "AugmentA: Patient-specific Augmented Atrial model Generation Tool" 
  authors:
    - family-names: Azzolin
      given-names: Luca
    - family-names: Eichenlaub
      given-names: Martin
    - family-names: Nagel
      given-names: Claudia
    - family-names: Nairn
      given-names: Deborah
    - family-names: Sánchez
      given-names: Jorge
    - family-names: Unger
      given-names: Laura
    - family-names: Dössel
      given-names: Olaf 
    - family-names: Jadidi
      given-names: Amir
    - family-names: Loewe
      given-names: Axel
  doi: 10.1101/2022.02.13.22270835
  journal: "medRxiv preprint"
  year: 2022

GitHub Events

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  • Push event: 6
  • Public event: 2

Dependencies

poetry.lock pypi
  • certifi 2023.7.22
  • charset-normalizer 3.2.0
  • contourpy 1.1.0
  • cycler 0.11.0
  • fonttools 4.42.0
  • h5py 3.7.0
  • idna 3.4
  • importlib-resources 6.0.1
  • joblib 1.3.2
  • kiwisolver 1.4.4
  • markdown-it-py 3.0.0
  • matplotlib 3.7.2
  • mdurl 0.1.2
  • meshio 5.3.4
  • numpy 1.25.2
  • packaging 23.1
  • pandas 1.5.3
  • pillow 10.0.0
  • platformdirs 3.10.0
  • pooch 1.7.0
  • pygments 2.16.1
  • pymeshfix 0.16.2
  • pymeshlab 2022.2.post4
  • pyparsing 3.0.9
  • python-dateutil 2.8.2
  • pytz 2023.3
  • pyvista 0.41.1
  • requests 2.31.0
  • rich 13.5.2
  • scikit-learn 1.2.2
  • scipy 1.9.3
  • scooby 0.7.2
  • six 1.16.0
  • threadpoolctl 3.2.0
  • transformations 2022.9.26
  • urllib3 2.0.4
  • vtk 9.2.6
  • zipp 3.16.2
pyproject.toml pypi
  • h5py ~3.7.0
  • meshio ~5.3.4
  • pandas ~1.5.2
  • pymeshfix >=0.16.1,<0.17.0
  • pymeshlab ~2022.2.post2
  • python ^3.9
  • scikit-learn ~1.2.1
  • transformations ~2022.9.26
requirements.txt pypi
  • h5py ==3.4.0
  • numpy <=1.19.3
  • pandas <=1.1.4
  • pymeshfix ==0.15.0
  • pymeshlab ==2021.7
  • sklearn <=0.24.2
  • transformations ==2021.6.6
  • vtk ==9.0.3