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Analysis of institutional authors

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October 31, 2024
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Rotation-invariant multi-contrast non-local means for MS lesion segmentation

Publicated to: NeuroImage-Clinical. 8 376-389 - 2015-01-01 8(), DOI: 10.1016/j.nicl.2015.05.001

Authors:

Guizard, N; Coupé, P; Fonov, VS; Manjón, JV; Arnold, DL; Collins, DL
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Affiliations

CNRS, PICTURA Res Grp, UMR 5800, Lab Bordelais Rech Informat - Author
McGill Univ, Montreal Neurol Inst, Montreal - Author
Univ Politecn Valencia, IBIME Res Grp, ITACA Inst, Med Imaging Area - Author
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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. (C) 2015 The Authors. Published by Elsevier Inc.
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Keywords

AccuracyAlgorithmAlgorithmsArticleAtlas selectionAutomationBrainBrain imagesComputer assisted diagnosisHumanHumansImage interpretation, computer-assistedInternal validityMagnetic resonance imagingMagnetic-resonance imagesMathematical analysisMathematical modelModelMr-imagesMriMs lesionsMsgcMulti-contrastMultiple sclerosisNon-localNuclear magnetic resonance imagingPatch-basedPathologyPriority journalProceduresRegistrationSclerosis lesionsSegmentationSupervisedValidation processWhite-matter lesions

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal NeuroImage-Clinical due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2015, it was in position 3/14, thus managing to position itself as a Q1 (Primer Cuartil), in the category Neuroimaging.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.26. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.88 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-03, the following number of citations:

  • WoS: 50
  • Scopus: 61
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-03:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 73.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 73 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 4.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Canada; France.

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