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antspynet

Pre-trained models and utilities for deep learning on medical images in Python

https://github.com/antsx/antspynet

Science Score: 59.0%

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Keywords

convolutional-neural-networks deep-learning python

Keywords from Contributors

neuroimaging medical-imaging insight-toolkit image-registration open-science image-analysis medical-image-processing interactive itk embedded
Last synced: 6 months ago · JSON representation

Repository

Pre-trained models and utilities for deep learning on medical images in Python

Basic Info
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  • Stars: 228
  • Watchers: 9
  • Forks: 29
  • Open Issues: 4
  • Releases: 0
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convolutional-neural-networks deep-learning python
Created over 6 years ago · Last pushed 7 months ago
Metadata Files
Readme License Code of conduct

README.md

PyPI - Downloads Contributor Covenant PubMed

Advanced Normalization Tools for Deep Learning in Python (ANTsPyNet)

A collection of deep learning architectures and applications ported to the Python language and tools for basic medical image processing. Based on keras and tensorflow with cross-compatibility with our R analog ANTsRNet. ANTsPyNet provides three high-level features:

  • A large collection of common deep learning architectures for medical imaging that can be initialized
  • Various pre-trained deep learning models to perform key medical imaging tasks
  • Utility functions to improve training and evaluating of deep learning models on medical images

Overview

Installation ### Binaries The easiest way to install ANTsPyNet is via pip. ``` python -m pip install antspynet ``` ### From Source Alternatively, you can download and install from source. ``` git clone https://github.com/ANTsX/ANTsPyNet cd ANTsPyNet python -m pip install . ```
Architectures ### Image voxelwise segmentation/regression - [U-Net (2-D, 3-D)](https://arxiv.org/abs/1505.04597) - [U-Net + ResNet (2-D, 3-D)](https://arxiv.org/abs/1608.04117) - [Dense U-Net (2-D, 3-D)](https://arxiv.org/pdf/1709.07330.pdf) ### Image classification/regression - [AlexNet (2-D, 3-D)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) - [VGG (2-D, 3-D)](https://arxiv.org/abs/1409.1556) - [ResNet (2-D, 3-D)](https://arxiv.org/abs/1512.03385) - [ResNeXt (2-D, 3-D)](https://arxiv.org/abs/1611.05431) - [WideResNet (2-D, 3-D)](http://arxiv.org/abs/1605.07146) - [DenseNet (2-D, 3-D)](https://arxiv.org/abs/1608.06993) ### Object detection ### Image super-resolution - [Super-resolution convolutional neural network (SRCNN) (2-D, 3-D)](https://arxiv.org/abs/1501.00092) - [Expanded super-resolution (ESRCNN) (2-D, 3-D)](https://arxiv.org/abs/1501.00092) - [Denoising auto encoder super-resolution (DSRCNN) (2-D, 3-D)]() - [Deep denoise super-resolution (DDSRCNN) (2-D, 3-D)](https://arxiv.org/abs/1606.08921) - [ResNet super-resolution (SRResNet) (2-D, 3-D)](https://arxiv.org/abs/1609.04802) - [Deep back-projection network (DBPN) (2-D, 3-D)](https://arxiv.org/abs/1803.02735) - [Super resolution GAN](https://arxiv.org/abs/1609.04802) ### Registration and transforms - [Spatial transformer network (STN) (2-D, 3-D)](https://arxiv.org/abs/1506.02025) ### Generative adverserial networks - [Generative adverserial network (GAN)](https://arxiv.org/abs/1406.2661) - [Deep Convolutional GAN](https://arxiv.org/abs/1511.06434) - [Wasserstein GAN](https://arxiv.org/abs/1701.07875) - [Improved Wasserstein GAN](https://arxiv.org/abs/1704.00028) - [Cycle GAN](https://arxiv.org/abs/1703.10593) - [Super resolution GAN](https://arxiv.org/abs/1609.04802) ### Clustering - [Deep embedded clustering (DEC)](https://arxiv.org/abs/1511.06335) - [Deep convolutional embedded clustering (DCEC)](https://xifengguo.github.io/papers/ICONIP17-DCEC.pdf)
Applications * [Brain applications](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#brain-applications) * [Multi-modal brain extraction](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#brain-extraction) * [Deep Atropos (Six-tissue brain segmentation)](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#deep-atropos) * [Cortical thickness](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#cortical-thickness) * [Desikan-Killiany-Tourville parcellation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#desikan-killiany-tourville-parcellation) * [Harvard-Oxford Atlas subcortical parcellation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#harvard-oxford-atlas-subcortical-parcellation) * [DeepFLASH (medial temporal lobe parcellation)](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#deepflash-medial-temporal-lobe-parcellation) * [Hippmapp3r (hippocampal segmentation)](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#hippmapp3r) * [Brain AGE](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#brain-age) * [Claustrum segmentation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#claustrum-segmentation) * [Hypothalamus segmentation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#hypothalamus-segmentation) * [Cerebellum morphology](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#cerebellum-morphology) * White matter hyperintensities segmentation * [SYSU](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#white-matter-hyperintensities-segmentation-sysu) * [Hypermapp3r](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#white-matter-hyperintensities-segmentation-hypermapp3r) * [SHIVA](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#white-matter-hyperintensities-segmentation-shiva) * [ANTsXNet](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#white-matter-hyperintensities-segmentation-antsxnet) * [Perivascular spaces segmentation (SHIVA)](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#perivascular-spaces-segmentation-shiva) * [Brain tumor segmentation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#brain-tumor-segmentation) * [MRA-TOF vessel segmentation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#mra-tof-vessel-segmentation) * [Lesion segmentation (WIP)](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#lesion-segmentation-wip) * [Whole head inpainting](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#whole-head-inpainting) * [Lung applications](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#lung-applications) * [Lung extraction](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#lung-extraction) * [Functional lung segmentation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#functional-lung-segmentation) * [Pulmonary artery segmentation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#pulmonary-artery-segmentation) * [Pulmonary airway segmentation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#pulmonary-airway-segmentation) * [CheXNet](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#chexnet) * [Mouse applications](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#mouse-applications) * [Mouse brain extraction](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#mouse-brain-extraction) * [Mouse brain parcellation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#mouse-brain-parcellation) * [Mouse cortical thickness](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#mouse-cortical-thickness) * [General applications](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#general-applications) * [MRI super resolution](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#mri-super-resolution) * [No reference image quality assesment using TID](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#no-reference-image-quality-assesment-using-tid) * [Full reference image quality assessment](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#full-reference-image-quality-assessment) * [Data augmentation](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#data-augmentation) * [Noise](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#noise) * [Histogram intensity warping](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#histogram-intensity-warping) * [Simulate bias field](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#simulate-bias-field) * [Random spatial transformations](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#random-spatial-transformations) * [Combined](https://gist.github.com/ntustison/12a656a5fc2f6f9c4494c88dc09c5621#combined)
Publications - Nicholas J. Tustison, Min Chen, Fae N. Kronman, Jeffrey T. Duda, Clare Gamlin, Mia G. Tustison, Michael Kunst, Rachel Dalley, Staci Sorenson, Quanxi Wang, Lydia Ng, Yongsoo Kim, and James C. Gee. The ANTsX Ecosystem for Mapping the Mouse Brain. [(biorxiv)](https://www.biorxiv.org/content/10.1101/2024.05.01.592056v1) - Nicholas J. Tustison, Michael A. Yassa, Batool Rizvi, Philip A. Cook, Andrew J. Holbrook, Mithra Sathishkumar, Mia G. Tustison, James C. Gee, James R. Stone, and Brian B. Avants. ANTsX neuroimaging-derived structural phenotypes of UK Biobank. _Scientific Reports_, 14(1):8848, Apr 2024. [(pubmed)](https://pubmed.ncbi.nlm.nih.gov/38632390/) - Nicholas J. Tustison, Talissa A. Altes, Kun Qing, Mu He, G. Wilson Miller, Brian B. Avants, Yun M. Shim, James C. Gee, John P. Mugler III, and Jaime F. Mata. Image- versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images. _Magnetic Resonance in Medicine_, 86(5):2822-2836, Nov 2021. [(pubmed)](https://pubmed.ncbi.nlm.nih.gov/34227163/) - Andrew T. Grainger, Arun Krishnaraj, Michael H. Quinones, Nicholas J. Tustison, Samantha Epstein, Daniela Fuller, Aakash Jha, Kevin L. Allman, Weibin Shi. Deep Learning-based Quantification of Abdominal Subcutaneous and Visceral Fat Volume on CT Images, _Academic Radiology_, 28(11):1481-1487, Nov 2021. [(pubmed)](https://pubmed.ncbi.nlm.nih.gov/32771313/) - Nicholas J. Tustison, Philip A. Cook, Andrew J. Holbrook, Hans J. Johnson, John Muschelli, Gabriel A. Devenyi, Jeffrey T. Duda, Sandhitsu R. Das, Nicholas C. Cullen, Daniel L. Gillen, Michael A. Yassa, James R. Stone, James C. Gee, and Brian B. Avants for the Alzheimer’s Disease Neuroimaging Initiative. The ANTsX ecosystem for quantitative biological and medical imaging. _Scientific Reports_. 11(1):9068, Apr 2021. [(pubmed)](https://pubmed.ncbi.nlm.nih.gov/33907199/) - Nicholas J. Tustison, Brian B. Avants, and James C. Gee. Learning image-based spatial transformations via convolutional neural networks: a review, _Magnetic Resonance Imaging_, 64:142-153, Dec 2019. [(pubmed)](https://www.ncbi.nlm.nih.gov/pubmed/31200026) - Nicholas J. Tustison, Brian B. Avants, Zixuan Lin, Xue Feng, Nicholas Cullen, Jaime F. Mata, Lucia Flors, James C. Gee, Talissa A. Altes, John P. Mugler III, and Kun Qing. Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification, _Academic Radiology_, 26(3):412-423, Mar 2019. [(pubmed)](https://www.ncbi.nlm.nih.gov/pubmed/30195415) - Andrew T. Grainger, Nicholas J. Tustison, Kun Qing, Rene Roy, Stuart S. Berr, and Weibin Shi. Deep learning-based quantification of abdominal fat on magnetic resonance images. _PLoS One_, 13(9):e0204071, Sep 2018. [(pubmed)](https://www.ncbi.nlm.nih.gov/pubmed/30235253) - Cullen N.C., Avants B.B. (2018) Convolutional Neural Networks for Rapid and Simultaneous Brain Extraction and Tissue Segmentation. In: Spalletta G., Piras F., Gili T. (eds) Brain Morphometry. Neuromethods, vol 136. Humana Press, New York, NY [doi](https://doi.org/10.1007/978-1-4939-7647-8_2)
License The ANTsPyNet package is released under an [Apache License](https://github.com/ANTsX/ANTsPyNet/blob/master/LICENSE.md).
Acknowledgements - We gratefully acknowledge the support of the NVIDIA Corporation with the donation of two Titan Xp GPUs used for this research. - We gratefully acknowledge the grant support of the [Office of Naval Research](https://www.onr.navy.mil) and [Cohen Veterans Bioscience](https://www.cohenveteransbioscience.org). - HCP-Aging templates acknowledgment: "The HCP-Aging 2.0 Release data used in this analysis was released on the NDA DOI: 10.15154/1520707. HCP-A was supported by the National Institute On Aging of the National Institutes of Health under Award Number U01AG052564 and by funds provided by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis."

Other resources

ANTsPyNet Documentation

ANTsXNet self-contained examples

Owner

  • Name: Advanced Normalization Tools Ecosystem
  • Login: ANTsX
  • Kind: organization
  • Location: Cambridge, MA; Philadelphia, PA; Southern California

Ecosystem supporting multi-modality biomedical image analysis across programming languages.

GitHub Events

Total
  • Create event: 24
  • Commit comment event: 3
  • Issues event: 26
  • Watch event: 24
  • Delete event: 25
  • Issue comment event: 70
  • Push event: 85
  • Pull request review event: 1
  • Pull request review comment event: 1
  • Pull request event: 55
  • Fork event: 3
Last Year
  • Create event: 24
  • Commit comment event: 3
  • Issues event: 26
  • Watch event: 24
  • Delete event: 25
  • Issue comment event: 70
  • Push event: 85
  • Pull request review event: 1
  • Pull request review comment event: 1
  • Pull request event: 55
  • Fork event: 3

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 891
  • Total Committers: 10
  • Avg Commits per committer: 89.1
  • Development Distribution Score (DDS): 0.134
Past Year
  • Commits: 93
  • Committers: 4
  • Avg Commits per committer: 23.25
  • Development Distribution Score (DDS): 0.183
Top Committers
Name Email Commits
Nick Tustison n****n@g****m 772
stnava s****a@g****m 74
Philip Cook c****a@p****u 28
ncullen93 n****n@m****e 6
dependabot[bot] 4****] 4
Karan Sindwani 6****n 2
Azeez Adebimpe a****e@o****m 2
Ravnoor Gill r****r 1
Dipterix Wang 8****x 1
akimbler a****9@f****u 1
Committer Domains (Top 20 + Academic)
fiu.edu: 1 med.lu.se: 1 pennmedicine.upenn.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 92
  • Total pull requests: 92
  • Average time to close issues: 4 months
  • Average time to close pull requests: 1 day
  • Total issue authors: 52
  • Total pull request authors: 10
  • Average comments per issue: 5.88
  • Average comments per pull request: 0.75
  • Merged pull requests: 83
  • Bot issues: 0
  • Bot pull requests: 7
Past Year
  • Issues: 14
  • Pull requests: 44
  • Average time to close issues: 27 days
  • Average time to close pull requests: about 13 hours
  • Issue authors: 11
  • Pull request authors: 4
  • Average comments per issue: 5.21
  • Average comments per pull request: 0.5
  • Merged pull requests: 40
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Pull Request Authors
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 2,957 last-month
  • Total docker downloads: 68
  • Total dependent packages: 2
  • Total dependent repositories: 10
  • Total versions: 18
  • Total maintainers: 2
pypi.org: antspynet

A collection of deep learning architectures ported to the python language and tools for basic medical image processing.

  • Versions: 18
  • Dependent Packages: 2
  • Dependent Repositories: 10
  • Downloads: 2,957 Last month
  • Docker Downloads: 68
Rankings
Docker downloads count: 3.0%
Dependent repos count: 4.6%
Dependent packages count: 4.8%
Stargazers count: 5.7%
Average: 6.1%
Forks count: 7.4%
Downloads: 11.0%
Maintainers (2)
stnava ntustison
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • antspyx *
  • keras *
  • requests *
  • tensorflow >=2.6
  • tensorflow_probability *
setup.py pypi
  • antspyx *
  • keras *
  • matplotlib *
  • numpy *
  • requests *
  • scikit-learn *
  • statsmodels *
  • tensorflow *
  • tensorflow-probability *