Validation of a semiautomated spinal cord segmentation method

Mendili, Mohamed-Mounir El; Chen, Raphaël; Tiret, Brice; Pélégrini‐Issac, Mélanie; Cohen‐Adad, Julien; Lehericy, Stéphane; Pradat, Pierre‐François; Benali, Habib

doi:10.1002/jmri.24571

Cited by 19 publications

(26 citation statements)

References 29 publications

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“…Several semi- or fully-automated segmentation methods have been proposed in the last decade for cervical CSA estimation (Losseff et al, 1996, Hickman et al, 2004, Tench et al, 2005, Zivadinov et al, 2008, Horsfield et al, 2010, McIntosh et al, 2011, Bergo et al, 2012, Chen et al, 2013, de Leener et al, 2014, Asman and Bryan, 2014, Taso et al, 2015, El Mendili et al, 2015, de Leener et al, 2016). While most methods present good performance, interpretation and comparison of results between different methods is seldom possible due to the use of different imaging datasets (usually in-house data), different MRI sequences, different ways to obtain gold standard segmentations (number of raters and consensus mask) and the use of various performance scores (2D/slice-wise or 3D/volumetric).…”

Section: Introductionmentioning

confidence: 99%

“…While most methods present good performance, interpretation and comparison of results between different methods is seldom possible due to the use of different imaging datasets (usually in-house data), different MRI sequences, different ways to obtain gold standard segmentations (number of raters and consensus mask) and the use of various performance scores (2D/slice-wise or 3D/volumetric). Recent cervical cord CSA segmentation methods have reached a performance close to manual segmentation (de Leener et al, 2014; Asman and Bryan, 2014, El Mendili et al, 2015, de Leener et al, 2016), but accurate GM segmentation remains a challenge. Moreover, there is a lack of publicly available datasets with GM/WM contrast and corresponding ground truth that facilitate a fair and reliable comparison across methods.…”

Section: Introductionmentioning

confidence: 99%

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Spinal cord grey matter segmentation challenge

et al. 2017

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An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spinal cord grey matter segmentation challenge

et al. 2017

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“…Recent studies by El Mendili et al 1421,. which are based on the threshold-based method (TbM) previously described by Losseff et al 2,.…”

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“…Over the last two decades, a number of semi-automated segmentation methods have been proposed for the estimation of spinal cord CSA2789101112131415, and more recently, methods that jointly segment both CSA and GM1617181920. Recent studies by El Mendili et al 1421,.…”

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Fully automated grey and white matter spinal cord segmentation

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Cardoso

Yiannakas

et al. 2016

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Axonal loss in the spinal cord is one of the main contributing factors to irreversible clinical disability in multiple sclerosis (MS). In vivo axonal loss can be assessed indirectly by estimating a reduction in the cervical cross-sectional area (CSA) of the spinal cord over time, which is indicative of spinal cord atrophy, and such a measure may be obtained by means of image segmentation using magnetic resonance imaging (MRI). In this work, we propose a new fully automated spinal cord segmentation technique that incorporates two different multi-atlas segmentation propagation and fusion techniques: The Optimized PatchMatch Label fusion (OPAL) algorithm for localising and approximately segmenting the spinal cord, and the Similarity and Truth Estimation for Propagated Segmentations (STEPS) algorithm for segmenting white and grey matter simultaneously. In a retrospective analysis of MRI data, the proposed method facilitated CSA measurements with accuracy equivalent to the inter-rater variability, with a Dice score (DSC) of 0.967 at C2/C3 level. The segmentation performance for grey matter at C2/C3 level was close to inter-rater variability, reaching an accuracy (DSC) of 0.826 for healthy subjects and 0.835 people with clinically isolated syndrome MS.

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“…Magnetic resonance imaging (MRI) has been increasingly used for providing valuable quantitative information about spinal cord morphometry, and microstructural data that reflects the degree of tissue injury and prognosis in neurodegenerative diseases and traumatic injuries . In parallel to the development of new acquisition methods for improving the quality of spinal cord MRI data, new image processing tools are being proposed for segmentation and morphometric analyses, to only cite a few (more can be found in Ref. .…”

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confidence: 99%