Au cours de ces travaux de thèse, nous avons discuté différents points fondamentaux pour la conception de méthodes de segmentation vasculaire, suivant trois axes d’étude : les modèles, les primitives et les schémas d’extraction. Cette démarche méthodologique nous a notamment apporté une meilleure appréhension de la vaste littérature du domaine. Nos développements ont porté sur une application médicale de premier ordre et particulière- ment complexe : la segmentation des artères coronaires en imagerie tomodensitométrique 3D (CT).
Les contributions de nos travaux incluent :
– un état de l’art étendu de la segmentation vasculaire 3D, articulé suivant nos 3 axes d’étude ;
– l’introduction d’un modèle géométrique discret, simple et particulièrement compact ; – le développement de primitives fondées sur le flux de gradient dotées d’un fort pou-
voir discriminant et d’un faible coût calculatoire ;
– l’introduction d’un modèle bayésien récursif pour les artères coronaires en imagerie CT, dont tous les termes sont appris d’une manière non paramétrique sur une base de segmentations manuelles ;
– le développement d’un premier schéma d’extraction fondé sur l’optimisation de che- mins minimaux 4D (ligne centrale et rayon) dont la métrique cumulative est dérivée de notre modèle bayésien ;
– le développement d’un second schéma d’extraction mettant en œuvre une méthode de suivi stochastique par filtre particulaire, introduisant notamment un nouveau schéma d’échantillonnage adaptatif.
Pour chaque composant et chaque méthode, des études de validation sur une grande quan- tité de données cliniques ont permis de démontrer qualitativement et quantitativement la pertinence de nos choix. Une attention particulière a également été portée sur la mise en œuvre pratique de nos méthodes, via l’introduction d’optimisations algorithmiques et d’im- plantation. Ainsi, nos algorithmes se fondent sur des approches relativement sophistiquées tout en maintenant de haut niveaux de performance calculatoire, les rendant utilisables sur des ordinateurs commerciaux.
Notre contribution applicative a porté principalement sur une tâche préliminaire mais cruciale, qui est la délinéation des structures vasculaires d’intérêt. Nos méthodes peuvent ainsi s’intégrer directement à certaines routines cliniques, par exemple pour l’aide à la vi-
sualisation et au diagnostic (voir figure 9.1). La fiabilité des résultats obtenus par nos schémas d’extraction peut également bénéficier à l’initialisation de traitements subsé- quents. Une extension naturelle serait une segmentation précise de la lumière vasculaire en vue d’applications de quantification des pathologies vasculaires. La figure9.2 illustre par exemple des résultats préliminaires obtenus via l’application de la technique de (Gulsun
and Tek,2006,2008b,a) pour une segmentation précise des sections vasculaires.
Finalement, notre cadre méthodologique est général et transférable à d’autres applica- tions vasculaires. La figure9.3illustre par exemple un résultat préliminaire obtenu avec nos méthode d’optimisation de chemins minimaux sur une angiographie CT des vaisseaux pé- riphériques. L’un des principaux obstacles reste la disponibilité de bases de segmentations manuelles pour les étapes d’apprentissage et de validation. La modélisation et l’exploita- tion d’informations spécifiques à l’application cible demeurent prépondérantes pour traiter des problèmes aussi complexes que ceux rencontrés dans le domaine de la segmentation vasculaire.
Vascular diseases are among the most important public health problems in developed countries (World Health Organization, 2008), motivating the tremendous amount of re- search dedicated to vascular imaging. In this context, the segmentation of vascular struc- tures is particularly valuable for diagnosis assistance, treatment and surgery planning. Isolating the vessels of interest from complex datasets is indeed a critical seminal step for their accurate visualization and for the quantification of pathologies. Unfortunately, most angiographic clinical routines still rely heavily on manual operations. Given the amount of data generated by modern 3D imaging modalities, such as computed tomography an- giography (CTA) and magnetic resonance angiography (MRA), manual segmentation can quickly add up to hours of processing. Automatic and semi-automatic image process- ing tools aim at easing and speeding up reviewing tasks, reducing the amount of manual interaction, lowering inter-operator variability and providing quantitative information.
Vascular segmentation is a particularly specific and challenging problem, which moti- vated a large amount of past and on-going dedicated research. In the context of automatic image understanding, the contributions of this thesis lie at different theoretical and prac- tical levels. We first focus on modeling issues, in other words, on the injection of prior knowledge with the aim of improving both the robustness and the accuracy of the seg- mentation. Second, we study the design of adequate vessel-dedicated features, i.e., what type of image information can be exploited to detect vascular structures and how this information is extracted in practice. Third, we investigate overall extraction schemes,
i.e., optimization algorithms driving the segmentation process, combining models and im-
age information. As an application of these methodological axes, this thesis focuses on a task of prime medical importance, the segmentation of coronary arteries from 3D cardiac computed tomography (CT) data.
This thesis work was co-directed by Isabelle Bloch and Elsa Angelini from Telecom ParisTech, Paris, France, in collaboration with Siemens Corporate Research, Princeton NJ, USA, under the supervision of Gareth Funka-Lea.
2.1
Medical Context
2.1.1 Coronary Arteries and Coronary Heart Disease
Coronary arteries are the blood vessels running along the heart surface and supplying the cardiac muscle (myocardium) with oxygen-rich blood. Coronary arteries branch off the ascending aorta at so-called ostia locations. One distinguishes two coronary trees (Fig.
superior vena cava
aorta
pulmonary artery left coronary artery (LCA)
left circumlex artery (LCX) left anterior descending branch (LAD) right coronary artery (RCA) aorta left coronary artery (LCA) left circumlex artery (LCX) left anterior descending branch (LAD) right coronary artery (RCA)
Figure 2.1: Anterior views of a human heart and principal vascular structures of interest. Left: stylized illustration (source: school of medicine, Yale university). Right: volume rendering of a cardiac CT angiogram (source: Siemens HealthCare).
2.1):
– the right coronary tree, whose main vascular branch is referred to as the right coro- nary artery (RCA);
– the left coronary tree, whose main vascular branch, the left coronary artery (LCA), separates between the left descending artery (LAD) and the left circumflex artery (LCX).
Coronary arteries play a critical role in the cardiac circulation system and are at the origin of severe medical conditions. Coronary heart disease (CHD) is currently the first cause of death in the US (Rosamond et al.,2008) and one of the leading cause of death worldwide
(World Health Organization,2008). CHD is caused by the accumulation of plaque in the
arteries (atherosclerosis). Atherosclerosis yields a reduction of the coronary blood flow, resulting in conditions such as anginas and heart attacks. The decrease in quality of the myocardium perfusion can lead to heart failures and arrhythmias.
2.1.2 Computed Tomography for the Assessment of Coronary Heart
Disease
Assessment of CHD is traditionally performed through invasive coronary angiographies (ICA) (Cademartiri et al.,2007), an X-ray procedure requiring the insertion of a catheter through the aorta for the injection of contrast agent (Fig. 2.2). Computed tomography angiography (CTA) proposes a non-invasive alternative to ICA (Mowatt et al., 2008). Following the injection of intra-venous contrast medium, cardiac CTA images consist of 3D acquisitions of the chest area. Modern multi-slice scanners reach sub-millimetric res- olutions1, enabling the accurate visualization of coronary arteries (Fig. 2.3). Whereas
traditional ICA enables only the visualization of the vascular lumen, CTA acquisitions also reveal calcified and soft plaque (Fig. 2.4), which may be clinically relevant cues for risk assessment (Cademartiri et al.,2007;Schaap et al.,2009a).
Despite its advantages, the use of CT technology for coronary angiography raises sev-
1. Typical spatial resolutions for 64-slice CT scanners are currently of the order of 0.3 × 0.3 × 0.4mm per voxel.
Figure 2.2: Traditional invasive coronary angiogram. Contrast agent (dye) is injected in the coronaries thanks a catheter, introduced through the aorta. Angiograms are obtained as 2D X-ray acquisitions. Source: Wikipedia.
Figure 2.3: Cardiac computed tomography angiogram (CTA). Top, left: multi-planar ref- ormation (MPR) axial view of a 2D slice through the 3D volume data. Top, right: coronal MPR view. Bottom, left: sagittal MPR view. Bottom, right: 3D volume rendering. Source: Siemens HealthCare, courtesy of Hong Kong Baptist Hospital Kowloon / Hong Kong, China.
Figure 2.4: Visualization of calcified and soft plaque in cardiac CTA data. Left: calcified plaque. Right: soft diffuse plaque. Curved planar reformation (CPR) views obtained from a delineation of the artery of interest. Source: Siemens HealthCare.
Figure 2.5: Left: volume rendering a cardiac CTA dataset. Right: maximum intensity projection (MIP) view of the same data.
eral concerns. First, CTA yields higher radiation doses than traditional ICA, and CTA acquisitions can suffer from motion, streaking (see Fig. 2.8, right) and partial volume arti- facts. Modern multislice scanners help alleviate such issues by enabling faster acquisition times, without sacrificing the resolution and the field of view. Second, the amount of data in 3D CTA acquisitions is large and despite the high image resolution, coronary arteries are relatively small and thin structures (typical radiuses ranging from 10 to 1 voxel for segments of interest). This second issue makes manual review tasks potentially long and tedious.
Visual assessment of CHD from CTA data can typically be performed by scrolling through axial, coronal and sagittal multi-planar reformation (MPR) views (see Fig. 2.3). More advanced visualization techniques include curved planar reformation (CPR) and maximal intensity projection (MIP) views. Examples of CPR views are given in Fig. 2.4
(a) (b)
(c) (d)
Figure 2.6: (a) Volume rendering of a right coronary artery with a stent, calcified plaque and stenosis. (b) Vessel extracted with the method from Chapter 7. (c) CPR view of the extracted branch. (d) Local tube MIP view of the extracted branch, with orthogonal MPR views of the stent.
and 2.6 (c). CPR techniques consist in generating 2D images where the entire length of the vessel of interest can be visualized, providing a global view of the target artery along with neighboring context. Different techniques can be employed to reconstruct CPR views
(Kanitsar et al.,2002). Global MIP visualization of coronary arteries is made difficult by
the presence of numerous hyper-intense structures other than coronary arteries: bones, pulmonary vessels and heart chambers in particular (Fig. 2.5). In practice, local MIP views can be obtained by constraining the projection in a small areas around a previously delineated vessel (Fig. 2.6 (d)).
Figure 2.7: Sample of centerline delineation for a full left coronary tree. RCA right atrium Coronary sinus RCA
Figure 2.8: Image processing challenges raised by CT coronaries. Left: pathologies such as soft and calcified plaque altering the geometry and appearance of coronary arteries. Middle: spiking acquisition artifacts. Right: distal RCA branch running close to the coronary sinus. MPR views.
pathologies, but both require the preliminary delineation of the vessel(s) of interest. Au- tomatic and semi-automatic techniques for the delineation of coronary arteries are thus particularly valuable in this clinical context. Last but not least, initial delineations can be exploited to greatly ease accurate lumen segmentation, enabling the automation of quantification tasks such as calcium scoring and stenosis grading.