Displaying 1 to 11 from 11 results

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.

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.

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.




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.

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.


dicom-rs - Pure Rust implementation of the DICOM standard

  •    Rust

An efficient and practical base library for DICOM compliant systems. At its core, this library is a pure Rust implementation of the DICOM representation format, allowing users to read and write DICOM objects over files and other sources, while remaining intrinsically fast and safe to use.

u-net-brain-tumor - U-Net Brain Tumor Segmentation in TensorFlow

  •    Python

🚀:Sep 2018 the data processing implementation in this repo is not the fastest, please use TensorFlow dataset API instead. This repo show you how to train a U-Net for brain tumor segmentation. By default, you need to download the training set of BRATS 2017 dataset, which have 210 HGG and 75 LGG volumes, and put the data folder along with all scripts.