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Neuroimage Clin. 2015 May 13;8:376-89. doi: 10.1016/j.nicl.2015.05.001. eCollection 2015.

Rotation-invariant multi-contrast non-local means for MS lesion segmentation.

Author information

1
Montreal Neurological Institute, McGill University, Canada.
2
Laboratoire Bordelais de Recherche en Informatique, Unité Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, 351, Talence, France.
3
IBIME Research Group, ITACA Institute, Universidad Politécnica de Valencia, Medical Imaging Area, Valencia, Spain.

Abstract

Multiple sclerosis (MS) lesion segmentation is crucial for evaluating disease burden, determining disease progression and measuring the impact of new clinical treatments. MS lesions can vary in size, location and intensity, making automatic segmentation challenging. In this paper, we propose a new supervised method to segment MS lesions from 3D magnetic resonance (MR) images using non-local means (NLM). The method uses a multi-channel and rotation-invariant distance measure to account for the diversity of MS lesions. The proposed segmentation method, rotation-invariant multi-contrast non-local means segmentation (RMNMS), captures the MS lesion spatial distribution and can accurately and robustly identify lesions regardless of their orientation, shape or size. An internal validation on a large clinical magnetic resonance imaging (MRI) dataset of MS patients demonstrated a good similarity measure result (Dice similarity = 60.1% and sensitivity = 75.4%), a strong correlation between expert and automatic lesion load volumes (R(2) = 0.91), and a strong ability to detect lesions of different sizes and in varying spatial locations (lesion detection rate = 79.8%). On the independent MS Grand Challenge (MSGC) dataset validation, our method provided competitive results with state-of-the-art supervised and unsupervised methods. Qualitative visual and quantitative voxel- and lesion-wise evaluations demonstrated the accuracy of RMNMS method.

KEYWORDS:

MRI; MS lesions; MSGC; Multi-contrast; Non-local; Patch-based; Segmentation; Supervised

PMID:
26106563
PMCID:
PMC4474283
DOI:
10.1016/j.nicl.2015.05.001
[Indexed for MEDLINE]
Free PMC Article

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