Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US

Ultrasound (US) can be used to assess brain development in newborns, as MRI is challenging due to immobilization issues, and may require sedation. Dilatation of the lateral ventricles in the brain is a risk factor for poorer neurodevelopment outcomes in infants. Hence, 3D US has the ability to assess the volume of the lateral ventricles similar to clinically standard MRI, but manual segmentation is time consuming. The objective of this study is to develop an approach quantifying the ratio of lateral ventricular dilatation with respect to total brain volume using 3D US, which can assess the severity of macrocephaly. Automatic segmentation of the lateral ventricles is achieved with a multi-atlas deformable registration approach using locally linear correlation metrics for US-MRI fusion, followed by a refinement step using deformable mesh models. Total brain volume is estimated using a 3D ellipsoid modeling approach. Validation was performed on a cohort of 12 infants, ranging from 2 to 8.5 months old, where 3D US and MRI were used to compare brain volumes and segmented lateral ventricles. Automatically extracted volumes from 3D US show a high correlation and no statistically significant difference when compared to ground truth measurements. Differences in volume ratios was 6.0 +/- 4.8% compared to MRI, while lateral ventricular segmentation yielded a mean Dice coefficient of 70.8 +/- 3.6% and a mean absolute distance (MAD) of 0.88 +/- 0.2mm, demonstrating the clinical benefit of this tool in paediatric ultrasound

Automatic segmentation of ventricular volume by 3D ultrasonography in post haemorrhagic ventricular dilatation among preterm infants

To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We trained a 2D convolutional neural network (CNN) for automatic segmentation ventricular volume from 3D US of preterm infants with PHVD. The method was validated with the Dice similarity coefficient (DSC) and the intra-class coefficient (ICC) compared to manual segmentation. The mean birth weight of the included patients was 1233.1 g (SD 309.4) and mean gestational age was 28.1 weeks (SD 1.6). A total of 152 serial 3D US from 10 preterm infants with PHVD were analysed. 230 ventricles were manually segmented. Of these, 108 were used for training a 2D CNN and 122 for validating the methodology for automatic segmentation. The global agreement for manual versus automated measures in the validation data (n=122) was excellent with an ICC of 0.944 (0.874-0.971). The Dice similarity coefficient was 0.8 (+/- 0.01). 3D US based ventricular volume estimation through an automatic segmentation software developed through deep learning improves the accuracy and reduces the processing time needed for manual segmentation using VOCAL. 3D US should be considered a promising tool to help deepen our current understanding of the complex evolution of PHVD

3D Ultrasound in the Management of Post Hemorrhagic Ventricle Dilatation

Enlargement of the cerebral ventricles is relatively common among extremely preterm neonates born before 28 weeks gestational age. One common cause of ventricle dilatation is post hemorrhagic ventricle dilatation following a bleed in the cerebral ventricles. While many neonates with PHVD will have spontaneous resolution of the condition, severe, persistent PHVD is associated with a greater risk of brain injury and morbidity later in life and left untreated can cause death. The current clinical management strategy consists of daily measurements of head circumference and qualitative interpretation of two-dimensional US images to detect ventricular enlargement and monitoring vital signs for indications increased intracranial pressure (i.e. apnea, bradycardia). Despite the widespread clinical use of these indicators, they do not have the specificity to reliably indicate when an intervention to remove some CSF is required to prevent brain damage. Early recognition of interventional necessity using quantitative measurements could help with the management of the disease, and could lead to better care in the future. Our objective was to develop and validate a three-dimensional ultrasound system for use within an incubator of neonates with PHVD in order to accurately measure the cerebral ventricle volume. This system was validated against known geometric phantoms as well as a custom ventricle-like phantom. Once validated, the system was used in a clinical study of 70 neonates with PHVD to measure the ventricle size. In addition to three-dimensional ultrasound, clinical ultrasound images, and MRIs were attained. Clinical measurements of the ventricles and three-dimensional ultrasound ventricle volumes were used to determine thresholds between neonates with PHVD who did and did not receive interventions based on current clinical management. We determined image based thresholds for intervention for both neonates who will receive an initial intervention, as well as those who will receive multiple interventions. Three-dimensional ultrasound based ventricle volume measurements had high sensitivity and specificity as patients with persistent PHVD have ventricles that increase in size faster than those who undergo resolution. This allowed for delineation between interventional and non-interventional patients within the first week of life. While this is still a small sample size study, these results can give rise to larger studies that would be able to determine if earlier intervention can result in better neurodevelopmental outcomes later in life

Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics

The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes. In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery. In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs. A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one

Volumétrie des ventricules latéraux chez le nouveau-né par segmentation automatique d’échographies 3D

Les nouvelles sondes échographiques d’ultrason (US) permettent d’acquérir des volumes de manière quasi instantanée et ce sans balayage. En comparaison avec les sondes deux dimensions (2D), ceci permet de diminuer le temps d’acquisition tout en ayant une qualité d’image similaire et potentiellement une meilleure confiance dans l’interprétation ou le diagnostic. L’évaluation ou le suivi du développement du cerveau et de la taille des ventricules est nécessaire pour plusieurs situations où le nouveau-né y est vulnérable comme dans des cas de naissances prématurées, d’hémorragie intraventriculaire (HIV), ou d’interventions chirurgicales. De plus, au niveau psychologique, une dilatation importante des ventricules latéraux est associée à divers troubles neurologiques ou retard de développement cognitif. Au niveau physique, une dilatation est associée à un développement altéré de la matière blanche et un volume anormal de matière grise corticale. Réaliser un suivi de la dilatation des ventricules latéraux peut donc permettre de déterminer si le nouveau-né est à risque d’avoir des effets négatifs sur son développement cognitif ou encore, pour les cas plus graves, si une intervention chirurgicale est nécessaire. Si une anormalité est trouvée en examen standard 2D US, une acquisition par imagerie par résonnance magnétique (IRM) peut être prescrite pour un examen approfondi. Cependant, l’IRM est difficile à utiliser pour imager le cerveau des nouveau-nés en raison de la contrainte d’immobilisation qui se traduit souvent par l’utilisation d’un sédatif. Donc une alternative pour suivre le développement du cerveau est d’utiliser une sonde matricielle avec une acquisition à travers la fontanelle qui est encore ouverte chez le nouveau-né de quelques mois. De plus, cette alternative permettrait de réaliser des analyses volumiques avec une méthode plus accessible et moins coûteuse que l’IRM. L’hypothèse du projet est que les images ultrasons acquises dans les premiers mois de vie du nouveau-né peuvent servir à évaluer le développement du cerveau et des ventricules latéraux en raison de la possibilité de réaliser des analyses volumiques quantitatives sur les volumes des ventricules latéraux et du cerveau. L’objectif du projet est donc de valider les volumes extraits des images tridimensionnelles (3D) US avec ceux de référence en IRM et de développer une méthodologie pour extraire automatiquement le volume du cerveau et des ventricules latéraux. Dans un premier temps, les ventricules latéraux sont segmentés manuellement sur les images IRM et 3D US acquises pour une première cohorte de patients. De plus, une méthode géométrique est développée afin d’estimer le volume du cerveau qui n’est pas inclus complètement par le faisceau d’acquisition. Cette méthode utilise un ellipsoïde pour modéliser la forme du cerveau où le volume peut donc être calculé avec les 3 semi-axes. Cette estimation du volume du cerveau est comparée à la mesure de circonférence de la tête, mesure pratiquée en clinique pour suivre le développement du cerveau, mais qui comporte plusieurs limitations. De plus, le ratio volumique ventricule-cerveau peut être calculé, ce qui permet d’évaluer la dilatation relative des ventricules par rapport au cerveau. Une étude comparative avec des tests statistiques est réalisée afin de valider les volumes extraits des images échographiques avec ceux de l’IRM qui représentent la vérité terrain. Les résultats démontrent qu’il n’y a aucune différence statistiquement significative entre les volumes extraits des images 3D US et des images IRM et qu’il y a une corrélation presque parfaite pour les ventricules latéraux (r=0.999) et une excellente corrélation pour le volume du cerveau (r=0.988). Ces analyses peuvent être réalisées sur les nouveau-nés jusqu’à l’âge d’environ 8 mois, âge où la fontanelle antérieure commence à se fermer empêchant les ondes acoustiques de passer. Dans un deuxième temps, le volume du cerveau est extrait automatiquement de l’image 3D US en isolant le cerveau du crâne et en appliquant la méthode géométrique développée. De plus, les ventricules latéraux ont été segmentés automatiquement sur 13 patients. Un recalage multi-atlas est d’abord réalisé avec des images IRM. Comme le recalage est multimodal, la différence des principes physiques des deux modalités d’imagerie le rend plus complexe et c’est pourquoi une métrique spécialement conçue pour le recalage US-IRM, la LC2 (Linear Correlation of Linear Combination) est utilisée. Les recalages sont suivis par une sélection des meilleures images et une fusion. Cependant, la LC2 ne permet pas de sélectionner automatiquement les meilleurs recalages entre différents atlas ou images IRM. Cette sélection est alors réalisée avec un terme de pondération de régions combiné à la LC2. La région ventriculaire est composée de deux sous-régions, la cavité de fluide qui est hypoéchogène et la choroïde plexus qui est hyperéchogène. Ce terme de pondération définit un poids pour chaque voxel de la région ventriculaire projetée, selon l’intensité et la position de ce voxel sur l’image échographique. Par la suite, deux algorithmes de fusion sont utilisés dans le projet, soit Majority Voting (MV) et STAPLE. Finalement, le résultat de la fusion est transformé en maillage et une déformation du maillage par minimisation d’énergie est implémentée pour finaliser la segmentation. Les résultats de segmentation démontrent une amélioration des résultats avec le terme de pondération par régions, la fusion, et le maillage déformable. Les résultats de segmentation finaux permettent d’avoir une précision adéquate en volume (DICE : 70.8%±3.6) et un faible écart des surfaces (Mean Absolute Distance : 0.88mm ± 0.20). Quant aux volumes du cerveau extraits automatiquement, ils ont une erreur absolue moyenne de 7.73% et une très bonne corrélation (r=0.942 ) comparativement à 3.12% et une excellente corrélation (r=0.988) lorsqu’ils sont extrait manuellement. De plus, les volumes des ventricules latéraux sont également extraits des segmentations (9.84% erreur absolue moyenne et r=0.848), ce qui permet de calculer le ratio volumique ventricule-cerveau automatiquement. Les travaux présentés dans ce mémoire ouvrent de nouvelles perspectives sur l’évaluation du développement du cerveau chez les nouveau-nés. Nos résultats démontrent qu’il est possible d’évaluer le volume du cerveau et des ventricules latéraux avec les nouvelles sondes matricielles d’échographies, ce qui pourrait augmenter l’accessibilité et la facilité des évaluations et des suivis réalisés en clinique. De plus, cela permet de calculer le ratio volumique ventriculecerveau afin d’évaluer la sévérité de la dilatation des ventricules relativement à la taille du cerveau.----------ABSTRACT New matrix-array ultrasound (US) probes allow neuroradiologists to acquire volumetric images almost instantly with no sweep of the region of interest. Compared to traditional 2D protocols, 3D US imaging decreases acquisition time without reducing image quality and could increase interpretation capabilities. Monitoring of the brain and lateral ventricles development is necessary especially in cases of premature birth, intraventricular hemorrhage (IVH) and surgical interventions. Significant ventricular dilatation is associated with some neurological disorders as well as lower scores on the Bayley scale of infant development and in some circumstances lower intelligence quotient (IQ). Furthermore, it is also associated with altered white matter development and abnormal volume of cortical gray matter. By monitoring the patients’ lateral ventricular dilatation, it is possible to determine if this is a risk factor for their cognitive development or if a surgical intervention is necessary in serious situations. If an abnormality is found with standard 2D US examinations, an MRI can be prescribed for a thorough examination. MRI is challenging with newborns due to immobilization issues, which requires most of the time sedation of the newborn. An alternative is to use recent matrix-array probes instead to perform non-invasive brain imaging through the fontanel. This will allow to perform volumetric analysis with an imaging method more accessible and less expensive than MRI. The project hypothesis is that it is possible to evaluate brain and ventricular development with the 3D US images and accomplish a series of quantitative volumetric measurements. The objective of this project is to validate the volumetric measurement of the 3D US images with the reference MRI and to develop a method to automatically extract the brain volume and segment the lateral ventricles in 3D US. The lateral ventricles volume is important to assess the progression of the dilatation before and after surgical interventions and to assess the severity of the dilatation. First, MRI and 3D US images are acquired for an initial cohort of 12 patients and the lateral ventricles are segmented manually in both imaging modalities. A geometric method is also developed in order to estimate the brain volume which is not fully captured by the limited US probe beam. This method uses an ellipsoid to model the brain shape where its volume is calculated with the 3 ellipsoid semi-axes. This brain volume estimation is compared to the head circumference (HC) which is a widely used method in clinical practice to follow brain development, although there are limitations associated with this approach. Ventricular-brain volume ratio is also calculated to assess the severity of the ventricular dilatation relatively to the brain size. A comparative study and statistical analysis are then undertaken to validate volumes obtained from 3D US images with those from MRI. Results show no statistically significant differences between the extracted MRI and 3D US volumes. Lateral ventricles have a near perfect correlation (r=0.999) and there is an excellent correlation for the brain volume (r=0.988). The difference in volume ratios was 6.0 ± 4.8% compared to MRI. Those analysis are possible on newborns and infants until they are approximately 8 months old, which is the age where the fontanelle starts to close, reducing the acoustic waves propagation. Secondly, the brain and lateral ventricles volumes are automatically extracted from the 3D US images. The brain volume is estimated with the same ellipsoid method after it has been aligned and stripped from the skull. The lateral ventricles were segmented on 13 patients using a multi-atlas registration pipeline with MRI images. Since this is a multimodal registration, a highly specific metric is used to register the MRI with the US images, the LC2 metric (Linear Correlation of Linear Combination). Then, the best registrations are selected for a label fusion but the LC2 alone doesn’t allow to automatically select the best registrations between several MRI images. An area weighting term is combined with the LC2 in order to improve the affine registration and to compare the registration results between several MRI images. The area weighting term assigns a weight to each voxel of the projected venricular area based on the position and intensity of the voxel on the US image. Indeed, the ventricular areas are divided in two areas, the fluid cavities which are hypoechoic and the plexus choroïd which is hyperechoic. These regions are used in the calculation of the weighting term. Two algorithms are tested for the label fusion, Majority Voting (MV) and STAPLE. Furthermore, the mesh is refined using deformable mesh model with an energy minimization process. The segmentation results are encouraging (DICE: 70.8±3.6, Mean Absolute Distance: 0.88± 0.20) and the extracted volumes have no statistically significant differences with the manual segmentations. The brain volumes have a mean absolute error with MRI volumes of 7.73% and a good correlation (r=0.942) when automatically segmented. As a comparison, the error was of 3.12% and the correlation excellent (r=0.988) with the manual measurements. In addition, the automatically extracted lateral ventricles volumes have a good correlation (r=0.848) with the manual segmentations and a mean absolute error of 9.84%. The methodology and results presented in this thesis show new perspectives and tools to help evaluate the infants’ brain development. This project demonstrates the potential of using new matrix-array US probes to assess brain and lateral ventricular volumes in newborns and infants which could be useful to facilitate monitoring of the lateral ventricles dilatation used for the macrocephaly diagnosis. Furthermore, it is possible to calculate the ventricular-brain volume ratio to assess the dilatation severity relatively to the brain volume