3DIVIMNET is a spatially-aware version of the IVIMNET model used for IVIM quantification. We test it on synthetic 3D images with known ground truth parameter maps.

Detection of Parkinson's disease using modified 3D UNET architecture

Pytorch implementation of 'Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosis' accepted to MICCAI BrainLes Worshop 2022

Example of brain tumor segmentation.

Easily start image segmentation with 3DUNet. Moreover, you can also utilize different modules, loss, and more.

A convolutional neural network segmenting fission yeast microscopy images. Application of 3D-Unet on microscopy videos.

Use 3D U-net and 2D Link net to predict membrane predition

Complete Docker Image including pre-processing, bronchinet and post-processing tools.

3D U-Net with tf.keras for Large-Model-Support or Unified Memory

Official Repository for the paper: Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Brain Tissue Segmentation on IBSR18 Dataset

PyTorch code for 3D-UNet for Cardiac MRI scan

Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

MICCAI2019: 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation

3D U-net, Attention U-net, Res U-net, Attention Res U-net, and MSRes U-net are implemented and compared for emulation of current density induced during transcranial direct current stimulation (tDCS).

Using the BraTS2020 dataset, we test several approaches for brain tumour segmentation such as developing novel models we call 3D-ONet and 3D-SphereNet, our own variant of 3D-UNet with more than one encoder-decoder paths.

Automatic segmentation of the liver and liver tumors in CT scans with 3D U-Net.

Notebooks that accompany medium articles - Build your own data science portfolio - the LUNA16 challenge.