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

<p align="middle"> <img src="docs/figures/coreANTsXNetTools.png" width="600" /> </p>

Overview

<details> <summary>Installation</summary>

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 .

</details> <!-- ## Quickstart The core functionality that ANTsPyNet provides is the ability to initialize a Deep Learning model based on our large collection of model architectures specifically tailored for medical images. You can then train these initialized models using your standard `keras` or `tensorflow` workflows. An example of initializing a deep learning model based on the is provided here: ```python from antspynet.architectures import create_autoencoder_model model = create_autoencoder_model((784, 500, 500, 2000, 10)) model.summary() ``` We also provide a collection of pre-trained models that can perform key medical imaging processing tasks such as brain extraction, segmentation, cortical thickness, and more. An example of reading a brain image using `ANTsPy` and then performing brain extraction using our pre-trained model in `ANTsPyNet` is presented here: ```python import ants import antspynet t1 = ants.image_read(antspynet.get_antsxnet_data('mprage_hippmapp3r')) seg = antspynet.brain_extraction(t1, modality="t1", verbose=True) ants.plot(t1, overlay=seg, overlay_alpha=0.5) ``` --> <details> <summary>Architectures</summary>

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

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)

</details> <details> <summary>Applications</summary>

• Brain applications

  • Multi-modal brain extraction
  • Deep Atropos (Six-tissue brain segmentation)
  • Cortical thickness
  • Desikan-Killiany-Tourville parcellation
  • Harvard-Oxford Atlas subcortical parcellation
  • DeepFLASH (medial temporal lobe parcellation)
  • Hippmapp3r (hippocampal segmentation)
  • Brain AGE
  • Claustrum segmentation
  • Hypothalamus segmentation
  • Cerebellum morphology
  • White matter hyperintensities segmentation
    • SYSU
    • Hypermapp3r
    • SHIVA
    • ANTsXNet
  • Perivascular spaces segmentation (SHIVA)
  • Brain tumor segmentation
  • MRA-TOF vessel segmentation
  • Lesion segmentation (WIP)
  • Whole head inpainting

• Lung applications

  • Lung extraction
  • Functional lung segmentation
  • Pulmonary artery segmentation
  • Pulmonary airway segmentation
  • CheXNet

• Mouse applications

  • Mouse brain extraction
  • Mouse brain parcellation
  • Mouse cortical thickness

• General applications

  • MRI super resolution
  • No reference image quality assesment using TID
  • Full reference image quality assessment

• Data augmentation

  • Noise
  • Histogram intensity warping
  • Simulate bias field
  • Random spatial transformations
  • [Combined](http