Spinal cord grey matter segmentation challenge

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

Spinal cord gray matter segmentation using deep dilated convolutions

Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and was also 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 and report state-of-the-art results in 8 out of 10 different evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure

Evaluation of Intra- and Interscanner Reliability of MRI Protocols for Spinal Cord Gray Matter and Total Cross-Sectional Area Measurements.

BackgroundIn vivo quantification of spinal cord atrophy in neurological diseases using MRI has attracted increasing attention.PurposeTo compare across different platforms the most promising imaging techniques to assess human spinal cord atrophy.Study typeTest/retest multiscanner study.SubjectsTwelve healthy volunteers.Field strength/sequenceThree different 3T scanner platforms (Siemens, Philips, and GE) / optimized phase sensitive inversion recovery (PSIR), T1 -weighted (T1 -w), and T2 *-weighted (T2 *-w) protocols.AssessmentOn all images acquired, two operators assessed contrast-to-noise ratio (CNR) between gray matter (GM) and white matter (WM), and between WM and cerebrospinal fluid (CSF); one experienced operator measured total cross-sectional area (TCA) and GM area using JIM and the Spinal Cord Toolbox (SCT).Statistical testsCoefficient of variation (COV); intraclass correlation coefficient (ICC); mixed effect models; analysis of variance (t-tests).ResultsFor all the scanners, GM/WM CNR was higher for PSIR than T2 *-w (P < 0.0001) and WM/CSF CNR for T1 -w was the highest (P < 0.0001). For TCA, using JIM, median COVs were smaller than 1.5% and ICC >0.95, while using SCT, median COVs were in the range 2.2-2.75% and ICC 0.79-0.95. For GM, despite some failures of the automatic segmentation, median COVs using SCT on T2 *-w were smaller than using JIM manual PSIR segmentations. In the mixed effect models, the subject was always the main contributor to the variance of area measurements and scanner often contributed to TCA variance (P < 0.05). Using JIM, TCA measurements on T2 *-w were different than on PSIR (P = 0.0021) and T1 -w (P = 0.0018), while using SCT, no notable differences were found between T1 -w and T2 *-w (P = 0.18). JIM and SCT-derived TCA were not different on T1 -w (P = 0.66), while they were different for T2 *-w (P < 0.0001). GM area derived using SCT/T2 *-w versus JIM/PSIR were different (P < 0.0001).Data conclusionThe present work sets reference values for the magnitude of the contribution of different effects to cord area measurement intra- and interscanner variability.Level of evidence1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:1078-1090

Deep semi-supervised segmentation with weight-averaged consistency targets

Recently proposed techniques for semi-supervised learning such as Temporal Ensembling and Mean Teacher have achieved state-of-the-art results in many important classification benchmarks. In this work, we expand the Mean Teacher approach to segmentation tasks and show that it can bring important improvements in a realistic small data regime using a publicly available multi-center dataset from the Magnetic Resonance Imaging (MRI) domain. We also devise a method to solve the problems that arise when using traditional data augmentation strategies for segmentation tasks on our new training scheme.Comment: 8 pages, 1 figure, accepted for DLMIA/MICCA

Fully automated grey and white matter spinal cord segmentation

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

Longitudinal changes of spinal cord grey and white matter following spinal cord injury

Objectives: Traumatic and non-traumatic spinal cord injury produce neurodegeneration across the entire neuraxis. However, the spatiotemporal dynamics of spinal cord grey and white matter neurodegeneration above and below the injury is understudied. Methods: We acquired longitudinal data from 13 traumatic and 3 non-traumatic spinal cord injury patients (8-8 cervical and thoracic cord injuries) within 1.5 years after injury and 10 healthy controls over the same period. The protocol encompassed structural and diffusion-weighted MRI rostral (C2/C3) and caudal (lumbar enlargement) to the injury level to track tissue-specific neurodegeneration. Regression models assessed group differences in the temporal evolution of tissue-specific changes and associations with clinical outcomes. Results: At 2 months post-injury, white matter area was decreased by 8.5% and grey matter by 15.9% in the lumbar enlargement, while at C2/C3 only white matter was decreased (-9.7%). Patients had decreased cervical fractional anisotropy (FA: -11.3%) and increased radial diffusivity (+20.5%) in the dorsal column, while FA was lower in the lateral (-10.3%) and ventral columns (-9.7%) of the lumbar enlargement. White matter decreased by 0.34% and 0.35% per month at C2/C3 and lumbar enlargement, respectively, and grey matter decreased at C2/C3 by 0.70% per month. Conclusions: This study describes the spatiotemporal dynamics of tissue-specific spinal cord neurodegeneration above and below a spinal cord injury. While above the injury, grey matter atrophy lagged initially behind white matter neurodegeneration, in the lumbar enlargement these processes progressed in parallel. Tracking trajectories of tissue-specific neurodegeneration provides valuable assessment tools for monitoring recovery and treatment effects

Longitudinal changes of spinal cord grey and white matter following spinal cord injury

Objectives: Traumatic and non-traumatic spinal cord injury produce neurodegeneration across the entire neuraxis. However, the spatiotemporal dynamics of spinal cord grey and white matter neurodegeneration above and below the injury is understudied. // Methods: We acquired longitudinal data from 13 traumatic and 3 non-traumatic spinal cord injury patients (8–8 cervical and thoracic cord injuries) within 1.5 years after injury and 10 healthy controls over the same period. The protocol encompassed structural and diffusion-weighted MRI rostral (C2/C3) and caudal (lumbar enlargement) to the injury level to track tissue-specific neurodegeneration. Regression models assessed group differences in the temporal evolution of tissue-specific changes and associations with clinical outcomes. // Results: At 2 months post-injury, white matter area was decreased by 8.5% and grey matter by 15.9% in the lumbar enlargement, while at C2/C3 only white matter was decreased (−9.7%). Patients had decreased cervical fractional anisotropy (FA: −11.3%) and increased radial diffusivity (+20.5%) in the dorsal column, while FA was lower in the lateral (−10.3%) and ventral columns (−9.7%) of the lumbar enlargement. White matter decreased by 0.34% and 0.35% per month at C2/C3 and lumbar enlargement, respectively, and grey matter decreased at C2/C3 by 0.70% per month. // Conclusions: This study describes the spatiotemporal dynamics of tissue-specific spinal cord neurodegeneration above and below a spinal cord injury. While above the injury, grey matter atrophy lagged initially behind white matter neurodegeneration, in the lumbar enlargement these processes progressed in parallel. Tracking trajectories of tissue-specific neurodegeneration provides valuable assessment tools for monitoring recovery and treatment effects

Optimized multi-echo gradient-echo magnetic resonance imaging for gray and white matter segmentation in the lumbosacral cord at 3 T

Atrophy in the spinal cord (SC), gray (GM) and white matter (WM) is typically measured in-vivo by image segmentation on multi-echo gradient-echo magnetic resonance images. The aim of this study was to establish an acquisition and analysis protocol for optimal SC and GM segmentation in the lumbosacral cord at 3 T. Ten healthy volunteers underwent imaging of the lumbosacral cord using a 3D spoiled multi-echo gradient-echo sequence (Siemens FLASH, with 5 echoes and 8 repetitions) on a Siemens Prisma 3 T scanner. Optimal numbers of successive echoes and signal averages were investigated comparing signal-to-noise (SNR) and contrast-to-noise ratio (CNR) values as well as qualitative ratings for segmentability by experts. The combination of 5 successive echoes yielded the highest CNR between WM and cerebrospinal fluid and the highest rating for SC segmentability. The combination of 3 and 4 successive echoes yielded the highest CNR between GM and WM and the highest rating for GM segmentability in the lumbosacral enlargement and conus medullaris, respectively. For segmenting the SC and GM in the same image, we suggest combining 3 successive echoes. For SC or GM segmentation only, we recommend combining 5 or 3 successive echoes, respectively. Six signal averages yielded good contrast for reliable SC and GM segmentation in all subjects. Clinical applications could benefit from these recommendations as they allow for accurate SC and GM segmentation in the lumbosacral cord

Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging

The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. This is the second review on the topic of g-ratio mapping using MRI. As such, it summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. Using simulations based on recently published data, this review demonstrates the relevance of the calibration step for three myelin-markers (macromolecular tissue volume, myelin water fraction, and bound pool fraction). It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest Editor