Fully automated grey and white matter spinal cord segmentation

Prados, Ferrán; Cardoso, M. Jorge; Yiannakas, Marios C.; Hoy, Luke; Tebaldi, E; Kearney, Hugh; Liechti, Martina D.; Miller, David H.; Ciccarelli, Olga; Wheeler-Kingshott, C; Ourselin, Sébastien

doi:10.1038/srep36151

Cited by 36 publications

(34 citation statements)

References 32 publications

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“…Finally, six teams submitted final results to the challenge and presented their method during the workshop.

Team 1 – University College London, led by FP, MJC, CWK and SO. Method name: Joint collaboration for spinal cord grey matter segmentation (Prados et al, 2016b), referred to as: JCSCS.

Team 2 – University of British Columbia, led by EL, TB and RT. Method name: Deepseg , referred to as: DEEPSEG.…”

Section: Methodsmentioning

confidence: 99%

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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“…Finally, six teams submitted final results to the challenge and presented their method during the workshop.

Team 1 – University College London, led by FP, MJC, CWK and SO. Method name: Joint collaboration for spinal cord grey matter segmentation (Prados et al, 2016b), referred to as: JCSCS.

Team 2 – University of British Columbia, led by EL, TB and RT. Method name: Deepseg , referred to as: DEEPSEG.…”

Section: Methodsmentioning

confidence: 99%

Spinal cord grey matter segmentation challenge

et al. 2017

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“…Mean cross-sectional area (CSA) and volume (CSV) were calculated for each vertebra and for the C2-C3 pair (CSA23 and CSV23), given the better sensitivity of this combined level to disease severity (Coulon et al, 2002;Liu et al, 2015;Prados et al, 2016;De Leener et al, 2017b). CSA is computed by counting pixels in each slice and then geometrically adjusting it multiplying by the angle (in degrees) between the spinal cord centerline and the inferior-superior direction.…”

Section: Spinal Cord Analysismentioning

confidence: 99%

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

Lorenzi

Palesi

Castellazzi

et al. 2020

Front. Cell. Neurosci.

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Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer's disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients' classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further

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“…From the acquisition standpoint, the advances in coil sensitivity 7 , multi-echo gradient echo sequences 8 , and phase-sensitive inversion recovery sequences 9 drastically improved the contrast-to-noise-ratio between the white and gray matter in the cord. From the analysis standpoint, the scientific community recently organized a collaboration effort called “Spinal Cord Gray Matter Segmentation Challenge” (SCGM Challenge) 6 to characterize the state-of-the-art and compare six independent developed methods 10 – 15 on a public available standard dataset created through the collaboration of four internationally recognized spinal cord imaging research groups (University College London, Polytechnique Montreal, University of Zurich and Vanderbilt University), providing therefore a ground basis for method comparison that was previously unfeasible.…”

Section: Introductionmentioning

confidence: 99%

Spinal cord gray matter segmentation using deep dilated convolutions

Perone

Calabrese

Cohen‐Adad

2018

Sci Rep

109

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Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully-automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge. We report state-of-the-art results in 8 out of 10 evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.

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Fully automated grey and white matter spinal cord segmentation

Cited by 36 publications

References 32 publications

Spinal cord grey matter segmentation challenge

Spinal cord grey matter segmentation challenge

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

Spinal cord gray matter segmentation using deep dilated convolutions

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