Featured tool/resource
PyNIDM
A Python library to manipulate the [Neuroimaging Data Model](http://nidm.nidash.org). Includes: - NIDM-Experiment Tools - BIDS MRI Conversion to NIDM - CSV File to NIDM Conversion - PyNIDM: REST API and Command Line Usage Additional NIDM-related Tools * NIDM-Terms <https://github.com/NIDM-Terms/terms> * NIDM-Terms Scicrunch Interface <https://scicrunch.org/nidm-terms> * Freesurfer stats -> NIDM <https://github.com/repronim/segstats_jsonld> * FSL structural segmentation -> NIDM <https://github.com/ReproNim/fsl_seg_to_nidm> * ANTS structural segmentation -> NIDM <https://github.com/ReproNim/ants_seg_to_nidm>
Featured tool/resource
DeepBraTumIA
DeepBraTumIA is a deep learning based software tool for automated segmentation of glioblastoma multiform (GBM) multisequence MR images. As input the software requires the following MRI sequences: T1-weighted, T2-weighted, T1-contrast, FLAIR. The software enables automated skull stripping and voxel-wise segmentation of brain tumor and its main sub-compartments of necrotic, contrast-enhancing and edema. A batch mode is enabled for processing of many cases. DeepBraTumIA analyses hardware resources and adapts its memory and CPU/GPU requirements accordingly. Segmentations can be exported along with volumetric measurements of tumor compartments. The tool can be used on pre- and post-operative cases, but better performance is known for pre-operative cases.
Featured tool/resource
NAMIC
The National Alliance for Medical Image Computing (NA-MIC) is a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who develop computational tools for the analysis and visualization of medical image data. The purpose of the Center is to provide the infrastructure and environment for the development of computational algorithms and open-source technologies, and then oversee the training and dissemination of these tools to the medical research community.
Featured tool/resource
Causality Challenge EEG Data
The data consists of 1000 examples of bivariate data for 6000 time points. Each example is a superposition of a signal (of interest) and noise. The signal is constructed from a unidirectional bivariate AR-model of order 10 with (otherwise) random AR-parameters and uniformly distributed input. The noise is constructed of three independent sources, generated with 3 univariate AR-models with random parameters and uniformly distributed input, which were instantaneously mixed into the two sensors with a random mixing matrix. The relative strength of noise and signal was set randomly. The data were generated with this [Matlab code]. (Of course, the seeds for the random number generators chosen for the challenge data are confidential.)
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