Related Projects

ITK - Insight Segmentation and Registration Toolkit -- Mirror

  •    C++

The National Library of Medicine Insight Segmentation and Registration Toolkit (ITK), or Insight Toolkit, is an open-source, cross-platform C++ toolkit for segmentation and registration. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. The toolkit may be built from source using CMake.

NiftyNet - An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

  •    Python

NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.

Insight Segmentation and Registration Toolkit

  •    C++

ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. Developed through extreme programming methodologies, ITK employs leading-edge algorithms for registering and segmenting multidimensional data.

CTK - A set of common support code for medical imaging, surgical navigation, and related purposes.

  •    C++

The Common Toolkit is a community effort to provide support code for medical image analysis, surgical navigation, and related projects.


  •    C++

3D medical image platform for visualization and image processing. Segmentation with Levels sets. Surface reconstruction with marching Cubes, texture Mapping and Raycasting,DICOM support. Integration environment for VTK, ITK and vtkInria3D under wxWidgets

Medical Image Computing Workflow

  •    Python

Medical Image Computing Workflow (MICFlow) is a simple, easy but flexible, extensible and powerful workflow system to automate medical image computing tasks such as segmentation, registration and analysis. It's pure Python so can be used on any platform

Cornerstone - JavaScript library to display interactive medical images including but not limited to DICOM

  •    Javascript

Cornerstone.js delivers a complete web based medical imaging platform. The easiest way to build interactive medical imaging web applications. It supports High performance image display. Multi-threaded image decoding in Web Workers, Robust DICOM Parsing. Supports all transfer syntaxes. Supports WADO-URI and WADO-RS.

Visualization Toolkit

  •    C++

The Visualization Toolkit (VTK) is an open-source, freely available software system for 3D computer graphics, image processing and visualization. VTK supports a wide variety of visualization algorithms including: scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques such as: implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation.

vtk-js - Visualization Toolkit for the Web

  •    Javascript

VTK is an open-source software system for image processing, 3D graphics, volume rendering and visualization. VTK includes many advanced algorithms (e.g., surface reconstruction, implicit modelling, decimation) and rendering techniques (e.g., hardware-accelerated volume rendering, LOD control). The JavaScript implementation remain a subset of the actual C++ library but efforts will be made to easily port or compile native VTK code into WebAssembly to better blend both world. The origin of VTK is with the textbook "The Visualization Toolkit, an Object-Oriented Approach to 3D Graphics" originally published by Prentice Hall and now published by Kitware, Inc. (Third Edition ISBN 1-930934-07-6). VTK has grown (since its initial release in 1994) to a world-wide user base in the commercial, academic, and research communities.

Medical Data Segmentation Toolkit

  •    C++

MDSTk is a collection of 2D/3D image processing tools aimed at medical images. It contains routines for volume data processing (3D filtering, segmentation, etc.) as well as fast low-level vector graphics library for surface and tetrahedral meshing.


  •    C++

The Orfeo Toolbox is a C++ library for high resolution remote sensing image processing. It is developped by CNES in the frame of the ORFEO program. More information is available at It is based on the medical image processing library ITK and offers particular functionalities for remote sensing image processing in general and for high spatial resolution images in particular. Targeted algorithms for high resolution optical images (SPOT, Quickbird, Worldview, Landsat, Iko

eos - A lightweight 3D Morphable Face Model fitting library in modern C++11/14

  •    C++

eos is a lightweight 3D Morphable Face Model fitting library that provides basic functionality to use face models, as well as camera and shape fitting functionality. It's written in modern C++11/14. An experimental model viewer to visualise 3D Morphable Models and blendshapes is available here.

dicom - High Performance DICOM Medical Image Parser in Go

  •    Go

This is a library and command-line tool to read, write, and generally work with DICOM medical image files in native Go. The goal is to build a full-featured, high-performance, and readable DICOM parser for the Go community.

ITK-SNAP Medical Image Segmentation Tool


ITK-SNAP is a tool for segmenting anatomical structures in medical images. It provides an automatic active contour segmentation pipeline, along with supporting manual segmentation toolbox. ITK-SNAP has a full-featured UI aimed at clinical researchers.

Surface-Defect-Detection - 📈 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance

  •    Python

At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. Compared with the clear classification, detection and segmentation tasks in computer vision, the requirements for defect detection are very general. In fact, its requirements can be divided into three different levels: "what is the defect" (classification), "where is the defect" (positioning) And "How many defects are" (split).

u-net - U-Net: Convolutional Networks for Biomedical Image Segmentation

  •    Python

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on This deep neural network achieves ~0.57 score on the leaderboard based on test images, and can be a good staring point for further, more serious approaches.

All-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

  •    Python

The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

VTK - Mirror of Visualization Toolkit repository

  •    C++

VTK is an open-source software system for image processing, 3D graphics, volume rendering and visualization. VTK includes many advanced algorithms (e.g., surface reconstruction, implicit modelling, decimation) and rendering techniques (e.g., hardware-accelerated volume rendering, LOD control). VTK is used by academicians for teaching and research; by government research institutions such as Los Alamos National Lab in the US or CINECA in Italy; and by many commercial firms who use VTK to build or extend products.

Simd - C++ image processing library with using of SIMD: SSE, SSE2, SSE3, SSSE3, SSE4

  •    C++

The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC (big-endian), NEON for ARM.

vpp - Video++, a C++14 high performance video and image processing library.

  •    C++

The generic container imageNd<V, N> represents a dense N-dimensional rectangle set of pixels with values of type V. For convenience, image1d, image2d, image3d are respectively aliases to imageNd<V, 1>, imageNd<V, 2>, and imageNd<V, 3>. These types provide accesses to the pixel buffer and to other piece of information useful to process the image. In contrast to std::vector, assigning an image to the other does not copy the data, but share them so no accidental expensive deep copy happen.

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