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README.md

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ANTsRNet

A collection of deep learning architectures and applications ported to the R language and tools for basic medical image processing. Based on keras and tensorflow with cross-compatibility with our python analog ANTsPyNet.

Documentation page https://antsx.github.io/ANTsRNet/.

ANTsXNetTools

Architectures

Image voxelwise segmentation/regression

Image classification/regression

Object detection

Image super-resolution

Registration and transforms

Generative adverserial networks

Clustering

Applications

Miscellaneous


Installation

  • ANTsRNet Installation:
    • Option 1:
      $ R
      > devtools::install_github( "ANTsX/ANTsRNet" )
      
    • Option 2:
      $ git clone https://github.com/ANTsX/ANTsRNet.git
      $ R CMD INSTALL ANTsRNet
      

Publications

  • 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. ANTsX: A dynamic ecosystem for quantitative biological and medical imaging. (medrxiv)

  • 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. (pubmed)

  • 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)

  • 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)

  • 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)

  • 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

Acknowledgments

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Medical image analysis framework merging ANTsR and deep learning

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