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Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
This repository implements pytorch version of the modifed 3D U-Net from Fabian Isensee et al. participating in BraTS2017
Use of state of the art Convolutional neural network architectures including 3D UNet, 3D VNet and 2D UNets for Brain Tumor Segmentation and using segmented image features for Survival Prediction of patients through deep neural networks.
Segmentation deep learning ALgorithm based on MONai toolbox: single and multi-label segmentation software developed by QIMP team-Vienna.
Urban change model designed to identify changes across 2 timestamps
The U-Net Segmentation server (caffe_unet) for Docker
Medical images segmentation with 3D UNet GAN
Fully automatic brain tumor segmentation using the Modified 3DUNet architecture for Brats 2020 Challenge.
Segmentation of thoracic and lumbar spine using deep learning
Iterative Vertebrae Segmentation - VerSe dataset
3D segmentation of neurites in EM images.
MICCAI2019: 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
https://www.youtube.com/watch?v=0h1eezoZhEU&t=26s
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.
Tensorflow based framework for 3D-Unet with Knowledge Distillation
Using synthetic datasets to train an end-to-end CNN for 3D fault segmentation