Guidelines for Effective Automatic Multiple Sclerosis Lesion Segmentation by Magnetic Resonance Imaging

Giuseppe Placidi, Luigi Cinque, Matteo Polsinelli

Abstract

General constraints for automatic identification/segmentation of multiple sclerosis (MS) lesions by Magnetic Resonance Imaging (MRI) are discussed and guidelines for effective training of a supervised technique are presented. In particular, system generalizability to different imaging sequences and scanners from different manufacturers, misalignment between images from different modalities and subjectivity in generating labelled images, are indicated as the main limitations to high accuracy automatic MS lesions identification/segmentation. A convolutional neural network (CNN) based method is used by applying the suggested guidelines and preliminary results demonstrate the improvements. The method has been trained, validated and tested on publicly available labelled MRI datasets. Future developments and perspectives are also presented.

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Paper Citation


in Harvard Style

Placidi G., Cinque L. and Polsinelli M. (2020). Guidelines for Effective Automatic Multiple Sclerosis Lesion Segmentation by Magnetic Resonance Imaging.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 570-577. DOI: 10.5220/0009150705700577


in Bibtex Style

@conference{icpram20,
author={Giuseppe Placidi and Luigi Cinque and Matteo Polsinelli},
title={Guidelines for Effective Automatic Multiple Sclerosis Lesion Segmentation by Magnetic Resonance Imaging},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={570-577},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009150705700577},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Guidelines for Effective Automatic Multiple Sclerosis Lesion Segmentation by Magnetic Resonance Imaging
SN - 978-989-758-397-1
AU - Placidi G.
AU - Cinque L.
AU - Polsinelli M.
PY - 2020
SP - 570
EP - 577
DO - 10.5220/0009150705700577