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3d-cgan

3D conditional GAN for medical image segmentation

3d-medical-segmentation-gan

3D Liver Segmentation with GAN

3d-unet--tensorflow

3D Unet for Isointense Infant Brain Image Segmentation

3dunet

airway segmentation

accumulate

accumulate

ai-learn

人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域

airlab

Image registration laboratory for 2D and 3D image data

airwaysegmentation

Airway Segmentation Framework

blockchainlittlebook.com

区块链小白书

cnn-registration

A image registration method using convolutional neural network features.

coderelease_colornormalization

This is the MATLAB code for stain separation and color normalization in computational pathology (histopathological images)

cyclegan-medical-image-segmentation

Implementation of CycleGAN for unsupervised image segmentaion, performed on brain tumor scans

deepcell-tf

Deep Learning Library for Single Cell Analysis

deeplearning-segan-segmentation

This contains an implementation of the SeGAN model for semantic segmentation introduced in https://arxiv.org/pdf/1703.10239.pdf

deepreg

Medical image registration using deep learning

dicomvis

Simple 4-pane viewer for visualising series of DICOM images. Written in Python using VTK.

dsb2017

The solution of team 'grt123' in DSB2017

eat_tensorflow2_in_30_days

Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

fewshot_gan-unet3d

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

fish_register

Image registration of smFISH images.

fisheye

fisheye image calibration

fourier-mellin

Image Registration using Fourier-Mellin

graph-cut

Graph-cut Image Segmentation ---------------------------- Implements Boykov/Kolmogorov’s Max-Flow/Min-Cut algorithm for computer vision problems. Two gray-scale images have been used to test the system for image segmentation (foreground/background segmentation) problem. Steps: 1. defined the graph structure and unary and pairwise terms. For graph structure, i have used available packages/libraries such as PyMaxflow. 2. likelihood function for background and foreground has been generated. 3. General energy function consisting of unary and pairwise energy functionals have been written. 4. Likelihood maps (intensity map ranging from 0 to 1) for foreground and background have been displayed. 5. Use Boykov/Kolmogorov maxflow / mincut approach for solving the energy minimization problem. 6. Final segmentation have been displayed. Created an image for which the background pixels are red, and the foreground pixels have the color of the input image. Relevant paper can be found here: http://www.csd.uwo.ca/~yuri/Papers/pami04.pdf

grow_airway_tree

histopathology-datasets

Ressources of histopathology datasets

image-segmentation-using-pytorch

Pytorch图像分割

imcut

3D graph cut segmentation

imreg_dft

Image registration using discrete Fourier transform.

jackfrued-python-100-days

kears

深度学习库keras的教程和案例demo