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
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (14.3%) to scientific vocabulary
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
Metadata Files
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.

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
- Website: https://www.linkedin.com/in/onurunaall/
- Repositories: 1
- Profile: https://github.com/onurunaall
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
Total
- Push event: 6
- Public event: 2
Last Year
- Push event: 6
- Public event: 2
Dependencies
- 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
- 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
- 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