39 research outputs found
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
General Surface Energy for Spinal Cord and Aorta Segmentation
We present a new surface energy potential for the segmentation of cylindrical objects in 3D medical imaging using parametric spline active contours (a.k.a. spline-snakes). Our energy formulation is based on an optimal steerable surface detector. Thus, we combine the concept of steerability with spline-snakes that have open topology for semi-automatic segmentation. We show that the proposed energy yields segmentation results that are more robust to noise compared to classical gradient-based surface energies. We finally validate our model by segmenting the aorta on a cohort of 14 real 3D MRI images, and also provide an example of spinal cord segmentation using the same tool
Segmentación de hueso, músculo y grasa en volúmenes TAC mediante relajación convexa
La segmentación de tejido óseo, graso y muscular en volúmenes
TAC es de interés especial para cirujanos y radiólogos en el
diagnóstico de algunas enfermedades y en planificación
quirúrgica. Estos tejidos son muy difÃciles de delinear debido a
la presencia de múltiples y diferentes estructuras y a la similitud
de valores de Hounsfield con los órganos circundantes.
En este artÃculo se muestra un algoritmo automático para
implementar la segmentación de hueso, músculo y tejido adiposo.
La segmentación se lleva a cabo minimizando una función de
energÃa mediante relajación convexa. En trabajos previos con
esta técnica solo se habÃan considerado dos etiquetas (hueso y
músculo) y los métodos tenÃan grandes problemas para
segmentar con precisión el músculo esquelético debido a la
presencia de órganos internos con valores Hounsfield muy
similares a los del tejido muscular. En este trabajo se ha incluÃdo
el conocimiento previo sobre la distribución del músculo
esquelético en los volúmenes de TAC abdominal, torácico y pelvis
mediante la inclusión de una transformación de distancia binaria
en el cálculo de los términos de coste. Para evaluar el
rendimiento del algoritmo se ha utilizado una base de datos
pública y se han obtenido diferentes métricas, como DICE,
Jaccard, sensibilidad y exactitud para evaluar el rendimiento del
algoritmo. La técnica se ha comparado con una implementación
anterior de dos etiquetas, con técnicas de level-sets y con
umbralización. El algoritmo propuesto superó a los otros
métodos en todas las métricas consideradas.Ministerio de EconomÃa y Competitividad DPI2016- 81103-
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
Upper cervical cord atrophy is independent of cervical cord lesion volume in early multiple sclerosis: A two-year longitudinal study
Background: Upper cervical cord atrophy and lesions have been shown to be associated with disease and disability progression already in early relapsing-remitting multiple sclerosis (RRMS). However, their longitudinal relationship remains unclear.
Objective: To investigate the cross-sectional and longitudinal relation between focal T2 cervical cord lesion volume (CCLV) and regional and global mean upper cervical cord area (UCCA), and their relations with disability.
Methods: Over a two-year interval, subjects with RRMS (n = 36) and healthy controls (HC, n = 16) underwent annual clinical and MRI examinations. UCCA and CCLV were obtained from C1 through C4 level. Linear mixed model analysis was performed to investigate the relation between UCCA, CCLV, and disability over time.
Results: UCCA at baseline was significantly lower in RRMS subjects compared to HCs (p = 0.003), but did not decrease faster over time (p ≥ 0.144). UCCA and CCLV were independent of each other at any of the time points or cervical levels, and over time. Lower baseline UCCA, but not CCLV, was related to worsening of both upper and lower extremities function over time. Conclusion: UCCA and CCLV are independent from each other, both cross-sectionally and longitudinally, in early MS. Lower UCCA, but not CCLV, was related to increasing disability over time
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
Segmentation automatique de la moelle épinière sur des images de résonance magnétique par propagation de modèles déformables
RÉSUMÉ
Les lésions de la moelle épinière, induites par des traumas (e.g. accident de la route) ou
par des maladies neurodégénératives, touchent plus 85 000 personnes au Canada avec environ
4250 nouveaux cas chaque année1. Elles ont de plus un impact majeur sur la vie quotidienne des
personnes atteintes, en provoquant des pertes de sensibilité et de contrôle moteur dont la gravité
dépend de la taille et de l’emplacement des lésions. Bien qu’il existe des approches
thérapeutiques permettant d’améliorer la réhabilitation fonctionnelle des patients, toutes ces
approches se heurtent à une inconnue majeure : l’étendue des dégâts causés par les lésions. Un
diagnostic précoce et précis des maladies neurodégénératives touchant la moelle épinière
permettrait d’améliorer grandement l’efficacité de leurs traitements. Depuis de nombreuses
années, l’IRM a prouvé son potentiel dans le diagnostic et le pronostic des lésions de la moelle
épinière (Cadotte, 2011; Cohen-Adad et al., 2011). Ce domaine manque cependant encore
d’outils complètement automatisés permettant l’extraction et la comparaison de métriques
cliniques reliées à la structure de la moelle (aire de section transverse, volume, etc.). La
segmentation de la moelle épinière sur des images IRM anatomiques peut fournir des mesures
d’aires et de volumes de la moelle (Losseff et al., 1996) et peut quantifier son atrophie en cas de
maladies neurodégénératives telles que la sclérose en plaques (Chen et al., 2013) et la sclérose
latérale amyotrophique (Cohen-Adad et al., 2011).
Ce projet de maîtrise vise à développer une méthode de segmentation complètement
automatique de la moelle épinière, fonctionnant sur plusieurs types d’images IRM (pondérées en
T1 et en T2) et sur n’importe quel champ de vue (cervical ou thoracique), et permettant d’extraire
et de comparer des mesures précises de la moelle épinière. La revue de la littérature a permis de
mettre en évidence le manque de méthode de segmentation automatique de la moelle épinière
fonctionnant sur n’importe quel type de contraste et de champ de vue. Elle a toutefois fait
ressortir une série de propriétés intéressantes, dans les méthodes semi-automatiques existantes,
pouvant être combinées pour former une méthode complètement automatisée.----------ABSTRACT
Spinal cord lesions affects more than 85,000 people in Canada with about 4,250 new
cases every year. Lesions can be caused by traumatic injuries or by neurodegenerative diseases
such as multiple sclerosis. They have an important impact on a patient’s daily life, inducing loss
of sensibility or motor control in the human body. The extent of damages caused by a lesion
varies with the number of damaged spinal cord tracks, and depends on the size and the position of
the lesion within the spinal cord. Although therapeutic approaches for patient functional
rehabilitation exist, they all face an unknown variable: the extent of spinal cord lesions. A precise
and early diagnosis of neurodegenerative diseases would improve their treatment efficiency. For
a number of years, MRI has demonstrated its potential in the diagnosis and prognosis of spinal
cord lesions (Cadotte, 2011; Cohen-Adad et al., 2010). However, this research field still lacks of
fully automatized tools for the extraction and comparison of clinical metrics related to the spinal
cord structure (e.g. cross-sectional area, volumes). Spinal cord segmentation on anatomical MR
images can provide accurate area and volume measurements (Losseff et al., 1996) and could
quantify spinal cord atrophy caused by neurodegenerative diseases such as multiple sclerosis
(Chen et al., 2013) or amyotrophic lateral sclerosis (Cohen-Adad et al., 2011).
The objective of this Master’s project is to develop a fully automatic spinal cord
segmentation method, working on multiple MR contrasts and any field of view, able to extract
and compare accurate spinal cord measurements. The literature review pointed out the lack of
such a method but highlighted several interesting features in existing methods, that can be
combined to develop a new automatic segmentation algorithm.
The method developed in this project is based on the multi-resolution propagation of a
deformable model. First, the spinal cord position and orientation is detected in the image using an
elliptical Hough transform on multiple adjacent axial slices. A low-resolution tubular mesh is
then build around the detection point and direction and deformed on spinal cord edges by
minimizing an energy equation. An iterative process, composed by the duplication, translation,
orientation and deformation of the mesh, propagates the surface along the spinal cord. Finally, a
refinement and a global deformation of the surface provide accurate segmentation of the spinal
cord. Measurements can be directly extracted from the segmentation surface. The spinal canal
can also be segmented with our method by simply inversing the gradient in the image an