German Cancer Research Center
MIRP
MIRP is a python package for quantitative analysis of medical images. It focuses on processing images for integration with radiomics workflows. These workflows either use quantitative features computed using MIRP, or directly use MIRP to process images as input for deep learning models.
- Medical Image Processing
- python
- radiomics
- Python
- Jupyter Notebook
- R
- + 2
Medical Imaging Interaction Toolkit (MITK)
A free and open-source software for the development of interactive medical image processing applications. MITK provides a powerful and free application called the MITK Workbench, which allows users to view, process, and segment medical images.
- Artificial Intelligence
- C++
- Data Visualization
- + 7
- C++
- CMake
- C
- + 8
nnU-Net
Automatic configuration and training of U-Net-based segmentation pipelines. Works out-of-the-box for a broad range of datasets from all imaging domain! Supports 2D and 3D (multi-channel) images.
- 2D images
- 3D images
- semantic segmentation
- + 1
- Python
- Shell
pkgndep
A new metric named 'dependency heaviness' is proposed that measures the number of additional dependency packages that a parent package brings to its child package and are unique to the dependency packages imported by all other parents.
- Data analysis
- Data Visualization
- FAIR Data
- + 3
- R
- HTML
- JavaScript
- + 1
RadPlanBio
RadiationDosePlan- Image/Biomarker-Outcome-platform (RPB) is a collection of open source software systems integrated via portal to deliver a core software infrastructure necessary to support the operation of non-commercial trials unit.
- Clinical-Research
- Health
- Radiotherapy
- + 1
- Java
- HTML
- CSS
- + 1
Rankings Reloaded
Rankings Reloaded is an open-source toolkit for robust and accurate benchmark visualization and ranking. It is an out-of-the-box and open-source framework for analyzing and visualizing algorithm results in an accurate way. It uses challengeR and brings advantages of it with user-friendly interface.
- Data analysis
- Data Visualization
- Information
- + 1
- R
rGREAT
GREAT is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm, also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application.
- Data analysis
- Data Visualization
- FAIR Data
- + 3
- R
- C++
simona
This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.
- Data analysis
- Data Visualization
- FAIR Data
- + 3
- R
- C++
- JavaScript
- + 3
SIMPA - The toolkit for Simulation and Image Processing for Photonics and Acoustics
SIMPA facilitates realistic simulations for optical and acoustic imaging modalities by providing a communication layer between various forward and inverse models. Non-experts can easily create sensible simulations from default parameters and domain experts can set up a highly customisable pipeline.
- acoustic imaging
- open-source
- optical imaging
- + 2
- Python
- MATLAB
- Shell
- + 1
simplifyEnrichment
A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings.
- Data analysis
- Data Visualization
- FAIR Data
- + 3
- R
- CSS
SpatialData framework
SpatialData is an open and interoperable data framework for ingesting, representing, processing and visualizing spatial omics datasets.
- Data Visualization
- FAIR Data
- Multi omics
- + 3
- Python
- Dockerfile
spiralize
It visualizes data along an Archimedean spiral, makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.
- Data analysis
- Data Science
- Data Visualization
- + 3
- R
- JavaScript
- CSS