Package: antspynet
ANTsPyNetA collection of deep learning architectures and applications ported to the python language and tools for basic medical image processing. Based onkerasandtensorflowwith cross-compatibility with our R analogANTsRNet.Documentation pagehttps://antsx.github.io/ANTsPyNet/.For MacOS and Linux, may install with:pipinstallantspynetArchitecturesImage voxelwise segmentation/regressionU-Net (2-D, 3-D)U-Net + ResNet (2-D, 3-D)Dense U-Net (2-D, 3-D)Image classification/regressionAlexNet (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 detectionImage super-resolutionSuper-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 GANRegistration and transformsSpatial transformer network (STN) (2-D, 3-D)Generative adverserial networksGenerative adverserial network (GAN)Deep Convolutional GANWasserstein GANImproved Wasserstein GANCycle GANSuper resolution GANClusteringDeep embedded clustering (DEC)Deep convolutional embedded clustering (DCEC)ApplicationsMRI super-resolutionMulti-modal brain extractionT1T1"no brainer"FLAIRT2FABOLDT1/T2 infantSix-tissue Atropos brain segmentationCortical thicknessBrain ageHippMapp3r hippocampal segmentationSysu white matter hyperintensity segmentationHyperMapp3r white matter hyperintensity segmentationHypothalamus segmentationClaustrum segmentationDeep FlashDesikan-Killiany-Tourville cortical labelingCerebellum segmentation, parcellation, and thicknessMRI modality classificationLung extractionProtonCTFunctional lung segmentationNeural style transferImage quality assessmentTID2013KonIQ-10kMixture density networks (MDN)RelatedTraining scriptsInstallationANTsPyNet Installation:Option 1:$ git clone https://github.com/ANTsX/ANTsPyNet
$ cd ANTsPyNet
$ python setup.py installPublicationsNicholas J. Tustison, Michael A Yassa, Batool Rizvi, Andrew J. Holbrook, Mithra Sathishkumar, James C. Gee, James R. Stone, and Brian B. Avants. ANTsX neuroimaging-derived structural phenotypes of UK Biobank.(medrxiv)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)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)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)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, NYdoiAcknowledgmentsWe 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 theOffice of Naval ResearchandCohen Veterans Bioscience.