Displaying 1 to 20 from 37 results

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

  •    Javascript

Cornerstone is an open source project with a goal to deliver a complete web based medical imaging platform. This repository contains the Cornerstone Core component which is a lightweight JavaScript library for displaying medical images in modern web browsers that support the HTML5 canvas element. Cornerstone Core is not meant to be a complete application itself, but instead a component that can be used as part of larger more complex applications. See the OHIF Viewer for an example of using the various Cornerstone libraries to build a simple study viewer. Cornerstone Core is agnostic to the actual container used to store image pixels as well as the transport mechanism used to get the image data. In fact, Cornerstone Core itself has no ability to read/parse or load images and instead depends on one or more ImageLoaders to function.

Orthanc - DICOM server for healthcare and medical research

  •    C++

Orthanc aims at providing a simple, yet powerful standalone DICOM server. It is designed to improve the DICOM flows in hospitals and to support research about the automated analysis of medical images.

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.

Deep-Learning-for-Medical-Applications - Deep Learning Papers on Medical Image Analysis

  •    TeX

To the best of our knowledge, this is the first list of deep learning papers on medical applications. There are couple of lists for deep learning papers in general, or computer vision, for example Awesome Deep Learning Papers. In this list, I try to classify the papers based on their deep learning techniques and learning methodology. I believe this list could be a good starting point for DL researchers on Medical Applications.

fo-dicom - Fellow Oak DICOM for .NET, .NET Core, Universal Windows, Android, iOS, Mono and Unity

  •    CSharp

If fo-dicom is a vital component in your open-source or commercial application and/or you want to contribute to its continued success, please consider making a small monetary contribution. This library is licensed under the Microsoft Public License (MS-PL). See License.txt for more information.

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.

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.

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.

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 https://www.kaggle.com/c/ultrasound-nerve-segmentation. 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.

robot-surgery-segmentation - Wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge

  •    Jupyter

Here we present our wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge. In this work, we describe our winning solution for MICCAI 2017 Endoscopic Vision Sub-Challenge: Robotic Instrument Segmentation and demonstrate further improvement over that result. Our approach is originally based on U-Net network architecture that we improved using state-of-the-art semantic segmentation neural networks known as LinkNet and TernausNet. Our results shows superior performance for a binary as well as for multi-class robotic instrument segmentation. We believe that our methods can lay a good foundation for the tracking and pose estimation in the vicinity of surgical scenes.

Xvision - Chest Xray image analysis using Deep learning !

  •    Python

Chest Xray image analysis using Deep Learning and exploiting Deep Transfer Learning technique for it with Tensorflow. The maxpool-5 layer of a pretrained VGGNet-16(Deep Convolutional Neural Network) model has been used as the feature extractor here and then further trained on a 2-layer Deep neural network with SGD optimizer and Batch Normalization for classification of Normal vs Nodular Chest Xray Images.

cornerstoneTools - A framework for tools built on top of Cornerstone.

  •    Javascript

cornerstoneTools is a library built on top of cornerstone that provides a set of common tools needed in medical imaging to work with images and stacks of images.

RNifti - Fast R and C++ access to NIfTI images

  •    C

The NIfTI-1 format is a popular file format for storing medical imaging data, widely used in medical research and related fields. Conceptually, a NIfTI-1 file incorporates multidimensional numeric data, like an R array, but with additional metadata describing the real-space resolution of the image, the physical orientation of the image, and how the image should be interpreted. The latest development version of the package can always be installed from GitHub using the devtools package.

RNiftyReg - An R interface to the NiftyReg medical image registration library

  •    C++

The RNiftyReg package is an R-native interface to the NiftyReg image registration library developed within the Translational Imaging Group at University College London. The package incorporates the library, so it does not need to be installed separately, and it replaces the NiftyReg command-line front-end with a direct, in-memory bridge to R, based on Rcpp. This README file primarily covers version 2.0.0 of the package and later. The interface was substantially reworked in that version to make it more natural and less verbose, and earlier versions are incompatible. Information on moving from prior versions of RNiftyReg to 2.x is included at the end of this file.

ExtensionsIndex - Slicer extensions index


Think of the ExtensionsIndex as a repository containing a list of extension description files (*.s4ext) used by the Slicer extensions build system to build, test, package and upload extensions on an extensions server. Once uploaded on an extensions server, within Slicer, extensions can be installed using the extensions manager.

itk-jupyter-widgets - Interactive Jupyter widgets to visualize images in 2D and 3D

  •    Python

Interactive Jupyter widgets to visualize images in 2D and 3D. These widgets are designed to support image analysis with the Insight Toolkit (ITK), but they also work with other spatial analysis tools in the scientific Python ecosystem.

FAST - Framework for Heterogeneous Medical Image Computing and Visualization

  •    C++

FAST (Framework for Heterogeneous Medical Image Computing and Visualization) is an open-source cross-platform framework with the main goal of making it easier to do processing and visualization of medical images on heterogeneous systems (CPU+GPU). A detailed description of the framework design can be found on the project wiki or in the research article: FAST: framework for heterogeneous medical image computing and visualization. Erik Smistad, Mohammadmehdi Bozorgi, Frank Lindseth. International Journal of Computer Assisted Radiology and Surgery. February 2015.

som - Stanford open modules for python

  •    Python

These are basic Python based tools for working with data (likely images and text) on Google Cloud. The base API module implements a basic token/refresh authentication, and the identifiers module is an extension of that for the Stanford School of Medicine. For complete docs, please see our documentation base and examples that coincide with each.