Validating stroke lesion segmentation methods using MRI in children : transferability of deep learning models - Archive ouverte HAL Access content directly
Conference Papers Year : 2024

Validating stroke lesion segmentation methods using MRI in children : transferability of deep learning models

Validation de méthodes de segmentation de lésion AVC à partir d'IRM pédiatriques : transférabilité de modèles de deep learning

Abstract

Accurate segmentation of stroke lesions from paediatric brain MRI scans is a challenging task due to the heterogeneity in size, shape and texture of the injuries. Deep learning and atlas-based techniques developed for adult patients may lead to a reduced segmentation performance when applied to children because they do not account for the changes in brain shape that occur during childhood development. The objectives of this work are 2-folds: to investigate the learning transferability of stroke lesion segmentation models trained on the adult domain and applied to the children domain, and to identify the deep learning architecture that produces the most accurate segmentation results.
Fichier principal
Vignette du fichier
ISBI_ELHERMITTE.pdf (1.75 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04607398 , version 1 (10-06-2024)

Identifiers

  • HAL Id : hal-04607398 , version 1

Cite

Emma Lhermitte, Mickaël Dinomais, Rodrigo Araneda Oyaneder, Yannick Bleyenheuft, Andrea Guzetta, et al.. Validating stroke lesion segmentation methods using MRI in children : transferability of deep learning models. ISBI 2024 - 21st IEEE International Symposium on Biomedical Imaging, May 2024, Athènes, Greece. ⟨hal-04607398⟩
0 View
0 Download

Share

Gmail Mastodon Facebook X LinkedIn More