![]() ![]() It implements the symmetric Local Correlation Coefficient (LCC) as a similarity measure, and thus it is unbiased with respect to local linear intensity bias of the images. LCClogDemons is an accurate and robust diffeomorphic registration framework based on the log-Demons. It is mostly intended for the research community to help develop newer algorithms, but can also be used as an efficient prototyping tool. ![]() SOFA is an Open Source framework primarily targeted at real-time simulation, with an emphasis on medical simulation. It has a robust visualization mechanism of any type of data, as well as a complete handling of time sequences of data. It was funded by CardioSense3D project and aims at providing the researcher with a set of tools for pre-processing data and to visualize results of cardiac simulations. MUSIC is a software developed in collaboration with the IHU LIRYC in order to propose functionalities dedicated to cardiac interventional planning and guidance, based on the medInria software platform.ĬardioViz3D targets research in cardiac imaging. MUSIC: Multi-modality Platform for Specific Imaging in Cardiology medInria is available for Microsoft Windows, Linux Fedora / Ubuntu, Mac OS X, and is fully multi-threaded. Efforts have been made to simplify the user interface, while keeping high-level algorithms. medInria aims at providing to clinicians state-of-the-art algorithms dedicated to medical image processing and visualization. MedInria is a free medical imaging software developed at Inria. It can also simulate realistic variation of intensity. At its core, it implements a biophysical model of brain deformation with atrophy in Alzheimer’s disease. The tool was developed to generate synthetic time-series structural MRIs with specified ground truth atrophy. For more details about the method, the model, and the performance, please refer to the paper 3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial (available here) is a C++ based simulator tool that can simulate specified volume changes in different parts of the image. The code of the method and the instructions for use are also available on this GitHub page. A package containing the code of the method, the pre-trained weights of the model, and the instructions for use can be downloaded here. Using deep learning with the spatial propagation of the segmentation, the method is both consistent and robust. For more details about the method, the model, and the performance, please refer to the paper Explainable Cardiac Pathology Classification on Cine MRI with Motion Characterization by Semi-Supervised Learning of Apparent Flow.ĬardiacSegmentationPropagation is a Python-based tool for cardiac image segmentation. Using deep learning, the method is both fast and lightweight. CardiacMotionFlow is a Python-based tool for 2D cardiac MRI registration (or apparent flow generation), cardiac motion extraction, and cardiac pathology classification. ![]()
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