Automated Brain Lobe Segmentation and Feature Extraction from Multiple Sclerosis Lesions Using Deep Learning

Nada Haj Messaoud, Nada Haj Messaoud, Rim Ayari, Asma Ben Abdallah, Mohamed Hedi Bedoui

2024

Abstract

This study focuses on automating the segmentation of brain lobes in MRI images of Multiple Sclerosis (MS) lesions to extract crucial features for predicting disability levels. Extracting significant features from MRI images of MS lesions is indeed a complex task due to the variability in lesion characteristics and the detailed nature of MRI images. Furthermore, all these studies required continuous patient monitoring. Therefore, our contribution lies in proposing an approach for the automatic segmentation of brain lobes and the extraction of lesion features (number, size, location, etc.) to predict disability levels in MS patients. To achieve this, we introduced a model inspired by U-Net to perform the segmentation of different brain lobes, aiming to accurately locate the MS lesions. We utilized two private and public databases and achieved an average mean IoU score of 0.70, which can be considered encouraging. Following the segmentation phase, approximately 7200 features were extracted from the MRI scans of MS patients.

Paper Citation

in Harvard Style

Haj Messaoud N., Ayari R., Ben Abdallah A. and Bedoui M. (2024). Automated Brain Lobe Segmentation and Feature Extraction from Multiple Sclerosis Lesions Using Deep Learning. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 532-540. DOI: 10.5220/0012390700003660

in Bibtex Style

@conference{visapp24,
author={Nada Haj Messaoud and Rim Ayari and Asma Ben Abdallah and Mohamed Hedi Bedoui},
title={Automated Brain Lobe Segmentation and Feature Extraction from Multiple Sclerosis Lesions Using Deep Learning},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={532-540},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012390700003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Automated Brain Lobe Segmentation and Feature Extraction from Multiple Sclerosis Lesions Using Deep Learning
SN - 978-989-758-679-8
AU - Haj Messaoud N.
AU - Ayari R.
AU - Ben Abdallah A.
AU - Bedoui M.
PY - 2024
SP - 532
EP - 540
DO - 10.5220/0012390700003660
PB - SciTePress