Habitat Sampler (HaSa, Community Version), an innovative tool that autonomously generates representative reference samples for predictive modelling of surface class probabilities. The R package can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales.

The main innovation of …

Hippocampal Shape and Thickness Analysis

Purpose:

This repository contains the Hipsta package, a collection of scripts for
hippocampal shape and thickness analysis as described in our recent publication.

Documentation:

Please see the documentation pages for a general overview, usage information and
examples, and output description. Brief usage information is also …

HoloWizard provides alogorithms for the phase retrieval of near-field holograms.
 
The phase problem is a well known ill-posed reconstruction problem of coherent lens-less microscopic imaging, where only the squared magnitude of a complex wavefront is measured by a detector while the phase information from the wave field is lost. To …

This collection of solutions brings together existing software solutions to recurring challenges along the imaging pipeline. Currently, it focuses mainly on visualization tasks, but will dynamically be extended by the Helmholtz Imaging Support & Engineering Unit at MDC Berlin.

Deep Learning Interactive Visualization

This project provides a software framework and all source code to learn a 3D convolutional neural network model for disease detection (e.g. Alzheimer's disease) and for visualization of contributing image regions with high relevance.

Further details on the procedures including samples, image processing, neural network modeling, …

Summary

JEMRIS is an extensible MRI (Magnetic Resonance Imaging) simulation framework which provides an MRI sequence development and simulation environment for the MRI community. The development was driven by the desire to achieve generality of simulated 3D MRI experiments reflecting modern MRI systems hardware. The accompanying computational burden is overcome …

Magnetic Resonance Imaging (MRI) is a routine tool in clinical diagnostics. In the past MRI was mainly used for subjective image reading by experienced radiologists, but the technology can be extended to quantitative biophysical tissue parameter mapping. Sensitizing the acquisition to the process of interest enables parameter estimation by subsequent …

At the Applied Machine Learning (AML) group, as part of the Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), we thought that using ML in research could be simpler.

In the same way as seaborn provides an abstraction of matplotlib’s functionality aiming for powerful data visualization with minor …

junifer - JUelich NeuroImaging FEature extractoR

About

junifer is a data handling and feature extraction library targeted towards neuroimaging data specifically functional MRI data.

It is currently being developed and maintained at the Applied Machine Learning group at Forschungszentrum Juelich, Germany. Although the library is designed for people working at …

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging.

Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an …

Kadi4Mat is the Karlsruhe Data Infrastructure for Materials Science, an open source software for managing research data. The goal of this project is to combine the ability to manage and exchange data, the repository, with the possibility to analyze, visualize and transform said data, the electronic lab notebook (ELN).

Kadi4Mat …

The process of acquiring microscopic images in life sciences often results in image degradation and corruption, characterised by the presence of noise and blur, which poses significant challenges in accurately analysing and interpreting the obtained data. We propse LUCYD, a novel method for the restoration of volumetric microscopy images that …

The Medical Imaging Interaction Toolkit (MITK) is a free open-source software
system for development of interactive medical image processing software. MITK
combines the Insight Toolkit (ITK) and the Visualization Toolkit (VTK) with an application framework.

The links below provide high-level and reference documentation targeting different
usage scenarios:

• Get a high-level …

Project MONAI is a set of open-source, freely available collaborative frameworks built for accelerating research and clinical collaboration in Medical Imaging. The goal is to accelerate the pace of innovation and clinical translation by building a robust software framework that benefits nearly every level of medical imaging, deep learning research, …

The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls.

The reliable validation of algorithms is key for the translation of biomedical image analysis research into …

microbeSEG is a deep learning-based tool for instance segmentation of roundish objects in 2D and 2D+t data, e.g., microbes. The microbeSEG workflow covers training data creation (with pre-labeling) or import, model training, model evaluation, and the final segmentation of the data. OMERO is used for a comprehensive data management. For …

The Python package minterpy is an implementation of the multivariate interpolation algorithm (MIP) by Hecht et al.^[1] and thereby provides software solutions that lift the curse of dimensionality from interpolation tasks. As interpolation occurs as the bottleneck of most computational challenges, minterpy aims to free empirical sciences from their …

Medical Image Radiomics Processor

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 neural networks and other deep learning …

MONAI Label is an intelligent image labeling and learning tool that uses AI assistance to reduce the time and effort of annotating new datasets. By utilizing user interactions, MONAI Label trains an AI model for a specific task and continuously learns and updates that model as it receives additional annotated …