A 3D hierarchical multimodal detection and segmentation method for multiple sclerosis lesions in MRI - Archive ouverte HAL Access content directly
Book Sections Year : 2016

A 3D hierarchical multimodal detection and segmentation method for multiple sclerosis lesions in MRI

(1) , (2) , (3) , (1)
1
2
3

Abstract

In this paper, we propose a novel 3D method for multiple sclerosis segmentation on FLAIR Magnetic Resonance images (MRI), based on a lesion context-based criterion performed on a max-tree representation. The detection criterion is refined using prior information from other available MRI acquisitions (T2, T1, T1 enhanced with Gadolinium and DP). The method has been tested on fifteen patients su↵ering from multiple sclerosis. The results show the ability of the method to detect almost all lesions. However, the algorithm also provides false detections.
Fichier principal
Vignette du fichier
Urien et al, MSSEG Challenge Proceedings.pdf (846.26 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inserm-01417465 , version 1 (15-12-2016)

Identifiers

  • HAL Id : inserm-01417465 , version 1

Cite

Hélène Urien, Irène Buvat, Nicolas Rougon, Isabelle Bloch. A 3D hierarchical multimodal detection and segmentation method for multiple sclerosis lesions in MRI. Proceedings of the 1st MICCAI Challenge on Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure — MICCAI-MSSEG, pp.69-74, 2016. ⟨inserm-01417465⟩
267 View
141 Download

Share

Gmail Facebook Twitter LinkedIn More