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/.

Architectures

Image voxelwise segmentation/regression

• U-Net (2-D, 3-D)
• U-Net + ResNet (2-D, 3-D)
• Dense U-Net (2-D, 3-D)

Image classification/regression

• AlexNet (2-D, 3-D)
• VGG (2-D, 3-D)
• ResNet (2-D, 3-D)
• ResNeXt (2-D, 3-D)
• WideResNet (2-D, 3-D)
• DenseNet (2-D, 3-D)

Object detection

• Single Shot MultiBox Detector (SSD) (2-D, 3-D)
  • SSD7: small 7-layer architecture
  • SSD300/SSD512: porting of original architectures

Image super-resolution

• Super-resolution convolutional neural network (SRCNN) (2-D, 3-D)
• Expanded super-resolution (ESRCNN) (2-D, 3-D)
• Denoising auto encoder super-resolution (DSRCNN) (2-D, 3-D)
• Deep denoise super-resolution (DDSRCNN) (2-D, 3-D)
• ResNet super-resolution (SRResNet) (2-D, 3-D)
• Deep back-projection network (DBPN) (2-D, 3-D)
• Super resolution GAN

Registration and transforms

• Spatial transformer network (STN) (2-D, 3-D)

Generative adverserial networks

• Generative adverserial network (GAN)
• Deep Convolutional GAN
• Wasserstein GAN
• Improved Wasserstein GAN
• Cycle GAN
• Super resolution GAN

Clustering

• Deep embedded clustering (DEC)
• Deep convolutional embedded clustering (DCEC)

Applications

• MRI super-resolution
• Multi-modal brain extraction
  • T1
  • T1 "no brainer"
  • FLAIR
  • T2
  • FA
  • BOLD
  • T1/T2 infant
• Six-tissue Atropos brain segmentation
• Cortical thickness
• Brain age
• Hippmapp3r hippocampal segmentation
• White matter hyperintensity segmentation
• Deep Flash
• Desikan-Killiany-Tourville cortical labeling
• Lung extraction
  • Proton
  • CT
• Functional lung segmentation
• Neural style transfer
• Image quality assessment
  • TID2013
  • KonIQ-10k

Miscellaneous

• Mixture density networks (MDN)

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, ..., Jaime F. Mata. Image- vs. histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images. (medrxiv)

• 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

• 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 and Cohen Veterans Bioscience.