This site provides two <strong>software tools related to "RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis" by Barquero et al. NeuroImage: Clinical (2020)</strong>. <strong>People using in part or fully this software should cite</strong>: (1) this repository, J. Najm et al, Zenodo (2023), DOI: 10.5281/zenodo.7962482 (2) the original journal paper, G. Barquero et al, Neuroimage Clinical (2020), DOI: 10.1016/j.nicl.2020.102412 To boost the use of RimNet by clinicians and medical imaging practitioners, we present two independent tools that predict presence or absence of paramagnetic rims over patches containing Multiple Sclerosis lesions when employing FLAIR and T2* Phase MRI: 1. <strong>3D-Slicer plugin</strong>: This tool is more appropriate for research and clinical environments where users need to manually inspect MR images. The tool supports the manual annotation procedure (storage of lesion coordinates, expert opinion and expert confidence) and at the same time provides RimNet prediction. If you want to use this plugin, download and install the Slicer-5.0.3-linux-amd64.zip contained within this repository. Instructions on how to use it are included in the zip file. 2. <strong>Dockerized version of RimNet</strong>: This tool is more appropriate for processing pipelines as it allows RimNet to be run, thereby obtaining predictions for possible rims by executing a single command. The trained model is the file rimnet-basics.zip contained within this repository. Once you have download the mode, to employ the docker please follow the instructions here: https://github.com/Medical-Image-Analysis-Laboratory/MS-Rims Further usage guidelines are in the README file. This work was funded by the Novartis Foundation for medical-biological Research (application 21A032), the Hasler Foundation (MSxplain project) and the CIBM Center for Biomedical Imaging, Switzerland. {"references": ["Barquero et al (2020). RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis, 10.1016/j.nicl.2020.102412"]}