Contributions à la segmentation d'image : phase locale et modèles statistiques

Ce document presente une synthèse de mes travaux apres these, principalement sur la problematique de la segmentation d’images

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Background and purpose: Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring. Materials and methods: A total of 103 clinical head and neck cancer cases, contoured using a commercial deep-learning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019–April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour. Results: The median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour. Conclusion: Auto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines

Ultrasound image segmentation using local statistics with an adaptative scale selection

La segmentation d'images est un domaine important dans le traitement d'images et un grand nombre d'approches différentes ent été développées pendant ces dernières décennies. L'approche des contours actifs est un des plus populaires. Dans ce cadre, cette thèse vise à développer des algorithmes robustes, qui peuvent segmenter des images avec des inhomogénéités d'intensité. Nous nous concentrons sur l'étude des énergies externes basées région dans le cadre des ensembles de niveaux. Précisément, nous abordons la difficulté de choisir l'échelle de la fenêtre spatiale qui définit la localité. Notre contribution principale est d'avoir proposé une échelle adaptative pour les méthodes de segmentation basées sur les statistiques locales. Nous utilisons l'approche d'Intersection des Intervalles de Confiance pour définir une échelle position-dépendante pour l'estimation des statistiques image. L'échelle est optimale dans le sens où elle donne le meilleur compromis entre le biais et la variance de l'approximation polynomiale locale de l'image observée conditionnellement à la segmentation actuelle. De plus, pour le model de segmentation basé sur une interprétation Bahésienne avec deux noyaux locaux, nous suggérons de considérer leurs valeurs séparément. Notre proposition donne une segmentation plus lisse avec moins de délocalisations que la méthode originale. Des expériences comparatives de notre proposition à d'autres méthodes de segmentation basées sur des statistiques locales sont effectuées. Les résultats quantitatifs réalisés sur des images ultrasonores de simulation, montrent que la méthode proposée est plus robuste au phénomène d'atténuation. Des expériences sur des images réelles montrent également l'utilité de notre approche.Image segmentation is an important research area in image processing and a large number of different approaches have been developed over the last few decades. The active contour approach is one of the most popular among them. Within this framework, this thesis aims at developing robust algorithms, which can segment images with intensity inhomogeneities. We focus on the study of region-based external energies within the level set framework. We study the use of local image statistics for the design of external energies. Precisely, we address the difficulty of choosing the scale of the spatial window that defines locality. Our main contribution is to propose an adaptive scale for local region-based segmen tation methods. We use the Intersection of Confidence Intervals approach to define this pixel-dependent scale for the estimation of local image statistics. The scale is optimal in the sense that it gives the best trade-off between the bias and the variance of a Local Polynomial Approximation of the observed image conditional on the current segmenta tion. Additionally, for the segmentation model based on a Bayesian interpretation with two local kernels, we suggest to consider their values separately. Our proposition gives a smoother segmentation with less mis-localisations Chan the original method.Comparative experiments of our method to other local region-based segmentation me thods are carried out. The quantitative results, on simulated ultrasound B-mode images, show that the proposed scale selection strategy gives a robust solution to the intensity inhomogeneity artifact of this imaging modality. More general experiments on real images also demonstrate the usefulness of our approach.COMPIEGNE-BU (601592101) / SudocSudocFranceF

SEGMENTATION BAYESIENNE D'IMAGES PAR UNE APPROCHE MARKOVIENNE MULTIRESOLUTION. APPLICATION A L'IMAGERIE ECHOCARDIOGRAPHIQUE ET ECHOGRAPHIQUE 2D ET 3D DU SEIN

VILLEURBANNE-DOC'INSA LYON (692662301) / SudocSudocFranceF

Ultrasound image segmentation: a survey

Abstract — This article reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting 10 papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem. Index Terms — Segmentation, ultrasound, B-scan, review. I

Segmentation des images ultrasonores par une approche de phase locale (application aux images échocardiographiques pédiatriques)

COMPIEGNE-BU (601592101) / SudocSudocFranceF

Automatic identification of regions of interest on renal tomographic images

We propose, in this paper, an original approach in a statistical framework, for fully automatic delineation of kidneys (healthy and pathological) in 2D CT images. Our approach has two main steps : a localisation step followed by a delineation step. The localisation step is guided by a statistically learned prior spatial model in one hand and a grey level prior model in a second hand. The second step, utilizes the localisation results in order to precisely delineate the kidney’s regions using a set of learned IF-THEN rules. The proposed approach is tested on clinically acquired images and promising results are obtained.Nous proposons, dans le présent papier, une approche originale dans un cadre statistique pour l’identification automatique des reins (sains et pathologiques) sur des images tomographiques bidimensionnelles (CT). Notre approche est constituée de deux phases : une phase de localisation suivie d’une phase de délimitation. La phase de localisation est guidée, d’une part, par un modèle a priori spatial et d’autre part, par un modèle a priori sur les niveaux de gris, statistiquement appris. La seconde phase consiste à utiliser les résultats de la localisation afin de délimiter la région du rein en utilisant un ensemble de règles. Cette approche est testée sur des images cliniquement acquises et des résultats satisfaisants sont obtenus

Segmentation d'images (étude comparative de différentes approches de contours déformables)

COMPIEGNE-BU (601592101) / SudocSudocFranceF