Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

Adaptive Vision System for Segmentation of Echographic Medical Images based on a Modified Mumford-Shah Functional

Abstract. This paper presents a novel adaptive vision system for accurate segmentation of tissue structures in echographic medical images. The proposed vision system incorporates a level-set deformable model based on a modified Mumford-Shah functional, which is estimated over sparse foreground and background regions in the image. This functional is designed so that it copes with the intensity inhomogeneity that characterizes echographic medical images. Moreover, a parameter tuning mechanism has been considered for the adaptation of the deformable model parameters. Experiments were conducted over a range of echographic images displaying abnormal structures of the breast and of the thyroid gland. The results show that the proposed adaptive vision system stands as an efficient, effective and nearly objective tool for the segmentation of echographic images.

Adaptive vision system for segmentation of echographic medical images based on a modified Mumford-Shah functional

This paper presents a novel adaptive vision system for accurate segmentation of tissue structures in echographic medical images. The proposed vision system incorporates a level-set deformable model based on a modified Mumford-Shah functional, which is estimated over sparse foreground and background regions in the image. This functional is designed so that it copes with the intensity inhomogeneity that characterizes echographic medical images. Moreover, a parameter tuning mechanism has been considered for the adaptation of the deformable model parameters. Experiments were conducted over a range of echographic images displaying abnormal structures of the breast and of the thyroid gland. The results show that the proposed adaptive vision system stands as an efficient, effective and nearly objective tool for segmentation of echographic images. © Springer-Verlag Berlin Heidelberg 2007

Filtrage anisotrope robuste et segmentation par B-spline snake : application aux images échographiques

Le contexte de ce travail est le traitement d'images échographiques. Plus précisément, on s'est intéressé au filtrage et à la segmentation automatique d'images dégradées par du speckle. La première partie concerne les travaux effectués sur le filtrage du speckle. Ils ont abouti à la conception d'une méthode de diffusion anisotrope robuste, nommée -diffusion. Elle se fonde sur un coefficient de diffusion original qui exploite lui-mˆeme la statistique du coefficient de variation et une adaptation de la fonction de Tukey. Un estimateur robuste du paramètre d'échelle de ce filtre est présenté. L'évolution de la diffusion est modélisée par une équation aux dérivées partielles s'appliquant sur l'enveloppe du signal brut, non compressée logarithmiquement. Cette approche permet de réduire le bruit des images échographiques, tout en préservant les structures importantes pour leur interprétation. Dans la deuxieme partie, nous présentons un contour actif paramétrique de type B-spline snake. L'étude de la continuité géométrique des B-splines nous permet de justifier le choix de l'énergie interne. Nous proposons deux nouvelles énergies externes qui exploitent notamment un champ de flux de vecteurs gradients, nommé s-GVF, calculé sur une carte de coefficients de variation locaux. Une fonction d'inhibition contrôle l'influence respective de ces deux énergies externe lors de l'évolution du snake. Enfin, nous proposons une nouvelle méthode d'initialisation automatique pour contour actif paramétrique. Une application au cas du filtrage des images echographiques et de la segmentation des cavités cardiaques est présentée. Les résultats démontrent une robustesse et une précision accrue par les modèles proposés par rapport aux techniques classiques de filtrage et segmentation par contours actifs. ABSTRACT : This thesis presents a robust model for speckle anisotropic filtering, and a parametric active contour model (B-spline snake) for the segmentation of images affected by speckle. First an original diffusion tensor is developed. It is based on the Tukey's error norm and on a local estimation of the coefficient of variation. The diffusion evolution is modelled by a partial derivative equation for raw images with no log-compression. This model reduces speckle while preserving important image features that are used by doctors to perform a diagnosis. Then we present a B-spline snake model with an external energy term that uses the amplitude and direction of the coefficient of variation gradient. The geometric continuity is guaranted by a uniform parametrisation and an internal energy term which penalizes the curve for irregular nodes spacing. An application to ultrasound image filtering and heart cavities detection is presented

Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability

International audienceComputational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or aging), to establish their variability, and to correlate this variability information with other functional, genetic or structural information. The Mathematical Foundations of Computational Anatomy (MFCA) workshop aims at fostering the interactions between the mathematical community around shapes and the MICCAI community in view of computational anatomy applications. It targets more particularly researchers investigating the combination of statistical and geometrical aspects in the modeling of the variability of biological shapes. The workshop is a forum for the exchange of the theoretical ideas and aims at being a source of inspiration for new methodological developments in computational anatomy. A special emphasis is put on theoretical developments, applications and results being welcomed as illustrations. Following the successful rst edition of this workshop in 20061 and second edition in New-York in 20082, the third edition was held in Toronto on September 22 20113. Contributions were solicited in Riemannian and group theoretical methods, geometric measurements of the anatomy, advanced statistics on deformations and shapes, metrics for computational anatomy, statistics of surfaces, modeling of growth and longitudinal shape changes. 22 submissions were reviewed by three members of the program committee. To guaranty a high level program, 11 papers only were selected for oral presentation in 4 sessions. Two of these sessions regroups classical themes of the workshop: statistics on manifolds and diff eomorphisms for surface or longitudinal registration. One session gathers papers exploring new mathematical structures beyond Riemannian geometry while the last oral session deals with the emerging theme of statistics on graphs and trees. Finally, a poster session of 5 papers addresses more application oriented works on computational anatomy

Méthodes multi-organes rapides avec a priori de forme pour la localisation et la segmentation en imagerie médicale 3D

With the ubiquity of imaging in medical applications (diagnostic, treatment follow-up, surgery planning. . . ), image processing algorithms have become of primary importance. Algorithms help clinicians extract critical information more quickly and more reliably from increasingly large and complex acquisitions. In this context, anatomy localization and segmentation is a crucial component in modern clinical workflows. Due to particularly high requirements in terms of robustness, accuracy and speed, designing such tools remains a challengingtask.In this work, we propose a complete pipeline for the segmentation of multiple organs in medical images. The method is generic, it can be applied to varying numbers of organs, on different imaging modalities. Our approach consists of three components: (i) an automatic localization algorithm, (ii) an automatic segmentation algorithm, (iii) a framework for interactive corrections. We present these components as a coherent processing chain, although each block could easily be used independently of the others. To fulfill clinical requirements, we focus on robust and efficient solutions. Our anatomy localization method is based on a cascade of Random Regression Forests (Cuingnet et al., 2012). One key originality of our work is the use of shape priors for each organ (thanks to probabilistic atlases). Combined with the evaluation of the trained regression forests, they result in shape-consistent confidence maps for each organ instead of simple bounding boxes. Our segmentation method extends the implicit template deformation framework of Mory et al. (2012) to multiple organs. The proposed formulation builds on the versatility of the original approach and introduces new non-overlapping constraintsand contrast-invariant forces. This makes our approach a fully automatic, robust and efficient method for the coherent segmentation of multiple structures. In the case of imperfect segmentation results, it is crucial to enable clinicians to correct them easily. We show that our automatic segmentation framework can be extended with simple user-driven constraints to allow for intuitive interactive corrections. We believe that this final component is key towards the applicability of our pipeline in actual clinical routine.Each of our algorithmic components has been evaluated on large clinical databases. We illustrate their use on CT, MRI and US data and present a user study gathering the feedback of medical imaging experts. The results demonstrate the interest in our method and its potential for clinical use.Avec l’utilisation de plus en plus répandue de l’imagerie dans la pratique médicale (diagnostic, suivi, planification d’intervention, etc.), le développement d’algorithmes d’analyse d’images est devenu primordial. Ces algorithmes permettent aux cliniciens d’analyser et d’interpréter plus facilement et plus rapidement des données de plus en plus complexes. Dans ce contexte, la localisation et la segmentation de structures anatomiques sont devenues des composants critiques dans les processus cliniques modernes. La conception de tels outils pour répondre aux exigences de robustesse, précision et rapidité demeure cependant un réel défi technique.Ce travail propose une méthode complète pour la segmentation de plusieurs organes dans des images médicales. Cette méthode, générique et pouvant être appliquée à un nombre varié de structures et dans différentes modalités d’imagerie, est constituée de trois composants : (i) un algorithme de localisation automatique, (ii) un algorithme de segmentation, (iii) un outil de correction interactive. Ces différentes parties peuvent s’enchaîner aisément pour former un outil complet et cohérent, mais peuvent aussi bien être utilisées indépendemment. L’accent a été mis sur des méthodes robustes et efficaces afin de répondre aux exigences cliniques. Notre méthode de localisation s’appuie sur une cascade de régression par forêts aléatoires (Cuingnet et al., 2012). Elle introduit l’utilisation d’informations a priori de forme, spécifiques à chaque organe (grâce à des atlas probabilistes) pour des résultats plus cohérents avec la réalité anatomique. Notre méthode de segmentation étend la méthode de segmentation par modèle implicite (Mory et al., 2012) à plusieurs modèles. La formulation proposée permet d’obtenir des déformations cohérentes, notamment en introduisant des contraintes de non recouvrement entre les modèles déformés. En s’appuyant sur des forces images polyvalentes, l’approche proposée se montre robuste et performante pour la segmentation de multiples structures. Toute méthode automatique n’est cependant jamais parfaite. Afin que le clinicien garde la main sur le résultat final, nous proposons d’enrichir la formulation précédente avec des contraintes fournies par l’utilisateur. Une optimisation localisée permet d’obtenir un outil facile à utiliser et au comportement intuitif. Ce dernier composant est crucial pour que notre outil soit réellement utilisable en pratique. Chacun de ces trois composants a été évalué sur plusieurs grandes bases de données cliniques (en tomodensitométrie, imagerie par résonance magnétique et ultrasons). Une étude avec des utilisateurs nous a aussi permis de recueillir des retours positifs de plusieurs experts en imagerie médicale. Les différents résultats présentés dans ce manuscrit montrent l’intérêt de notre méthode et son potentiel pour une utilisation clinique

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions

Micro-costing study of rituximab subcutaneous injection versus intravenous infusion in dutch setting

Background: Rituximab for subcutaneous (SC) administration has recently been approved for use in common forms of diffuse large B-cell lymphoma (DLBCL). This form of rituximab is supplied in ready-to-use vials that do not require individual dose adjustment. It is expected that SC-injection will shorten the treatment time per administration of rituximab in comparison with currently available intravenous (IV) infusion. Aims: The goal of this study is to identify and compare all direct costs of IV and SC rituximab given to the DLBCL patients in the Netherlands. Methods: Using a prospective, observational, bottom up, micro-costing study we collected primary data on the direct medical costs of the preparation, administration and acquisition of rituximab. Drug costs and spillage, labor costs, material costs and remaining daycare costs were identified using standardized forms, structured using guideline prices and compared for the IV and SC forms of rituximab. Results: Measurements were done on 53 administrations (33 IV and 20 SC). The mean total costs of the IV infusion were €2174, and €1907 for the SC injection. The estimated difference of €267 per administration was mainly due to spillage costs and differences in chair time, related daycare costs and drug costs. Summary and Conclusions: Rituximab administered in the form of SC injection is less costly than its IV form. Taking into account their equal effectiveness, favorable pharmacoeconomic profile of SC rituximab can result in significant savings when transferred to the total DLBCL population in the Netherlands

Trial efficacy vs real world effectiveness in first line treatment of multiple myeloma

Background: Large randomized clinical trials (RCT) are the foundation of the registration of newly developed drugs. A potential problem with RCTs is that the inclusion/exclusion criteria will make the population different from the actual population treated in real life. Hence, it is important to understand how the results from the RCT can be generalized to a general population. Aims: The primary aim of the present study was to assess the generalizability of the large 1st line RCTs in Multiple Myeloma (MM) to the Nordic setting and to understand potential difference and magnitude in outcomes between RCTs and patients treated in standard care in the Nordics. Methods: A retrospective analysis was performed on an incident cohort of 2960 MM-patients from 24 hospitals in Denmark, Finland, Norway and Sweden. The database contained information on patient baseline characteristics, treatments and outcomes. Data from relevant 1st line MM RCTs was selected from the treatment MP (Waage, A., et al., Blood. 2010], MPT (Waage, A., et al., Blood. 2010) and VMP (San Miguel, J.F., et al., N Engl J Med, 2008) and baseline characteristics were compared to newly diagnosed Nordic MM treated patients. Potential difference in response and overall survival (OS) was estimated by adjusting the RWE population to the RCT population using matching adjusted indirect comparisons. Patients were matched on age (median approximated to mean), gender, calcium, beta2-microglobulin and ISS score 3. These variables were selected because they were reported in all trials and have previously been identified as having prognostic value. Results: Patients in the Nordic database treated with MP (n=880) had a response rate of (PD, NR, PR, VGPR, ≥nCR) of (13%, 39%, 38%, 6%, 4%). After matching (n=347), the response rate was slightly worse (12%, 43%, 36%, 6%, 3%). This can be compared to the response rate from the RCT of (7%, 53%, 33%, 3%, 4%). OS for Nordic MP treated patients was 2.67 years (2.25-3.17). After matching the OS was 3.37 years (2.86-3.96) and this can be compared to the trial with OS 2.40 years (2.23-2.66). Patients treated with MPT (n=283) in the Nordic countries had a response rate of (5%, 14%, 52%, 20%, 9%). After matching (n=179) the response rate was slightly changed to (6%, 20%, 50%, 13% 11%). The corresponding RCT response results were 14%, 29%, 34%, 10%, and 13% respectively. OS for Nordic MPT treated patients was 4.15 years (3.73- 4.74). After matching the OS was 4.28 years (3.98-NA) years and compared to 2.42 years (2.08-3.17) OS observed in the corresponding trial. Patients treated with VMP (n=59) in the Nordic countries had a response rate of (4%, 5%, 40%, 18%, 33%). After matching (n=31) the response rate was improved to (8%, 11%, 28%, 8%, 45%). This corresponding response rates shown in the trial are 1%, 23%, 33%, 8%, and 33% respectively. OS for Nordic MP treated patients was 4.86 years (3.79-NA). After matching the OS was 4.86 years (4.86-NA) and this can be compared to the trial with OS 4.70 years. Summary and Conclusions: Surprisingly Nordic treated MM patients do very well compared to, and even better than, patients treated in RCTs. Since the OS for all tested treatments improves after matching to the RCT baseline characteristics, patients recruited to the RCTs seems to be a bit better than ordinary Nordic patents. The database used in the present study, and the used method, can be valuable for generalizing the results to the Nordic setting and estimating potential difference for future RCTs and Nordic MM treated patients. Future research should include different data cuts to see whether the analyses are biased by differences subsequent treatments applied in RCTs and clinical practice