Dataset or other product:

Streamline RimNet: Tools for Automatic Classification of Paramagnetic Rim Lesions in MRI of Multiple Sclerosis

cris.legacyId

303581

cris.virtual.department

LTS5

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301560

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296096

cris.virtual.sciperId

192459

cris.virtual.sciperId

124931

cris.virtual.unitManager

Thiran, Jean-Philippe

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6e4cbb1a-bc0d-45b5-bdb9-dfce4e44ef35

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d14a2673-0f1e-4a53-86fe-337c4da33940

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001d8c78-8b3b-4b9d-a9e3-9b01b71582c1

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001d8c78-8b3b-4b9d-a9e3-9b01b71582c1

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45eff4cb-75bb-4185-8a5d-d247e8652540

cris.virtualsource.unitManager

24256c18-3294-40df-91a5-eefdca985e4a

datacite.relatedIdentifier

https://infoscience.epfl.ch/record/280178

datacite.relatedIdentifier

https://doi.org/10.5281/zenodo.7962481

datacite.relatedIdentifier

https://github.com/Medical-Image-Analysis-Laboratory/MS-Rims

datacite.relationType

IsSupplementTo

datacite.relationType

IsNewVersionOf

datacite.relationType

IsCompiledBy

datacite.rights

openaccess

dc.contributor.author

Najm, Joe

dc.contributor.author

Macias Gordaliza, Pedro

dc.contributor.author

Barquero, Germán

dc.contributor.author

La Rosa, Francesco

dc.contributor.author

Molchanova, Nataliia

dc.contributor.author

Wynen, Maxence

dc.contributor.author

Absinta, Martina

dc.contributor.author

Maggi, Pietro

dc.contributor.author

Granziera, Cristina

dc.contributor.author

Bach Cuadra, Meritxell

dc.date.accessioned

2023-07-12T11:28:05

dc.date.available

2023-07-12T11:28:05

dc.date.created

2023-07-12

dc.date.issued

2023

dc.date.modified

2024-10-18T18:27:02.040303Z

dc.description.abstract

This site provides two 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). People using in part or fully this software should cite: (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. 3D-Slicer plugin: 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. Dockerized version of RimNet: 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"]}

dc.description.version

1

dc.identifier.doi

10.5281/zenodo.7962482

dc.identifier.uri

https://infoscience.epfl.ch/handle/20.500.14299/199023

dc.language.iso

en

dc.publisher

Zenodo

dc.relation

https://infoscience.epfl.ch/record/303581/files/Slicer-5.0.3-linux-amd64.zip

dc.relation

https://infoscience.epfl.ch/record/303581/files/rimnet-basics.zip

dc.relation

https://infoscience.epfl.ch/record/303581/files/AstreamlineRimNet.png

dc.subject

Multiple Sclerosis

dc.subject

Paramagnetic rim lesions

dc.subject

MRI

dc.subject

Deep learning

dc.subject

Supervised classification

dc.subject

Brain

dc.subject

Brain lesions

dc.subject

Docker

dc.subject

Manual annotations

dc.subject

Software

dc.title

Streamline RimNet: Tools for Automatic Classification of Paramagnetic Rim Lesions in MRI of Multiple Sclerosis

dc.type

dataset

dspace.entity.type

Product

dspace.file.type

n/a

dspace.file.type

n/a

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n/a

dspace.legacy.oai-identifier

oai:infoscience.epfl.ch:303581

epfl.curator.email

alessandra.bianchi@epfl.ch

epfl.lastmodified.email

alessandra.bianchi@epfl.ch

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Datasets

epfl.legacy.submissionform

DATASET

epfl.oai.currentset

datasets

epfl.writtenAt

EPFL

oaire.licenseCondition

CC BY

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