Automatic IVUS segmentation of atherosclerotic plaque with Stop & Go snake

Since the upturn of intravascular ultrasound (IVUS)as an imaging technique for the coronary arterysystem, much research has been done to simplify thecomplicated analysis of the resulting images. In thisstudy, an attempt to develop an automatic tissuecharacterization algorithm for IVUS images wasdone. We concentrated on the segmentation ofcalcium and soft plaque, because these structurespredict the extension and the vulnerability of theatherosclerotic disease, respectively. The first step in the procedure was the extraction of texture featureslike local binary patterns, co-occurrence matricesand Gabor filter banks. After dimensionalityreduction, the resulting feature space was used forclassification, constructing a likelihood map torepresent different coronary plaques. Theinformation in this map was organized using arecently developed geodesic snake formulation,the so-called Stop & Go snake. The novelty of ourstudy lies in this last step, as it was the first time to apply the Stop & Go snake to segment IVUSimages

Automatic IVUS segmentation of atherosclerotic plaque with Stop & Go snake

Since the upturn of intravascular ultrasound (IVUS)as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was done. We concentrated on the segmentation of calcium and soft plaque, because these structures predict the extension and the vulnerability of the atherosclerotic disease, respectively. The first step in the procedure was the extraction of texture features like local binary patterns, co-occurrence matrices and Gabor filter banks. After dimensionality reduction, the resulting feature space was used for classification, constructing a likelihood map to represent different coronary plaques. The information in this map was organized using a recently developed geodesic snake formulation,the so-called Stop & Go snake. The novelty of our study lies in this last step, as it was the first time to apply the Stop & Go snake to segment IVUS images

Automatic IVUS segmentation of atherosclerotic plaque with stop & go snake

Since the upturn of intravascular ultrasound (IVUS) as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was done. The first step was the extraction of texture features. The resulting feature space was used for classification, constructing a likelihood map to represent different coronary plaques. The information in this map was organized using a recently developed geodesic snake formulation, the so-called Stop & Go snake. The novelty of our study lies in this last step, as it was the first time to apply the Stop & Go snake to segment IVUS images

Hitting the right target : noninvasive localization of the subthalamic nucleus motor part for specific deep brain stimulation

Deep brain stimulation of the subthalamic nucleus (STN) has gained momentum as a therapy for advanced Parkinson’s disease. The stimulation effectively alleviates the patients’ typical motor symptoms on a long term, but can give rise to cognitive and psychiatric adverse effects as well. Based on primate studies, the STN has been divided into three functionally different parts, which were distinguished by their afferent and efferent connections. The largest part is the motor area, followed by an associative and a limbic area. The serious adverse effects on cognition and behavior occurring after deep brain stimulation are assumed to be caused by electrical current spread to the associative and limbic areas of the STN. Therefore, selective stimulation of the motor part of the STN seems crucial, both to obtain the best possible therapeutic effect on the motor symptoms and to minimize the debilitating effects on cognition and behavior. However, current medical imaging techniques do not yet facilitate the required accurate identification of the STN itself, let alone its different functional areas. The final target for DBS is still often adjusted using intraoperative electrophysiology. Therefore, in this thesis we aimed to improve imaging for deep brain stimulation using noninvasive MRI protocols, in order to identify the STN and its motor part. We studied the advantages and drawbacks of already available noninvasive methods to target the STN. This review did not lead to a straightforward conclusion; identification of the STN motor part remained an open question. In follow-up on this question, we investigated the possibility to distinguish the different functional STN parts based on their connectivity information. Three types of information were carefully analyzed in this thesis. First, we looked into the clustering of local diffusion information within the STN region. We visually inspected the complex diffusion profiles, derived from postmortem rat brain data with high angular resolution, and augmented this manual segmentation method using k-means and graph cuts clustering. Because the weighing of different orders of diffusion information in the traditionally used L2 norm on the orientation distribution functions (ODFs) remained an open issue, we developed a specialized distance measure, the so-called Sobolev norm. This norm does not only take into account the amplitudes of the diffusion profiles, but also their extrema. We showed it to perform better than the L2 norm on synthetic phantom data and real brain (thalamus) data. The research done on this topic facilitates better classification by clustering of gray matter structures in the (deep) brain. Secondly, we were the first to analyze the STN’s full structural connectivity, based on probabilistic fiber tracking in diffusion MRI data of healthy volunteers. The results correspond well to topical literature on STN projections. Furthermore, we assessed the structural connectivity per voxel of the STN seed region and discovered a gradient in connectivity to the premotor cortex within the STN. While going from the medial to the lateral part of the STN, the connectivity increases, confirming the expected lateral location of the STN motor part. Finally, the connectivity analysis produced evidence for the existence of a "hyperdirect" pathway between the motor cortex and the STN in humans, which is very useful for future research into stimulation targets. The results of these experiments indicate that it is possible to find the motor part of the STN as specific target for deep brain stimulation using structural connectivity information acquired in a noninvasive way. Third and last, we studied functional connectivity using resting state functional MRI data of healthy volunteers. The resulting significant clusters provided us with the first complete description of the STN’s resting state functional connectivity, which corresponds with the expectations based on available literature. Moreover, we performed a reverse regression procedure with the average time series signals in motor and limbic areas as principal regressors. The results were analyzed for each STN voxel separately and also showed mediolateral gradients in functional connectivity within the STN. The lateral STN part exhibited more motor connectivity, while the medial part seemed to be more functionally connected to limbic brain areas, as described in neuronal tracer studies. These results show that functional connectivity analysis also is a viable noninvasive method to find the motor part of the STN. The work on noninvasive MRI methods for identification of the STN and its functional parts, as presented in this thesis, thus contributes to future specific stimulation of the motor part of the STN for deep brain stimulation in patients with Parkinson’s disease. This may help to maximize the motor effects and minimize severe cognitive and psychiatric side effects

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Background and motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach

Segmentation automatique de la lumière des artères sur une séquence d'images intra vasculaires à l'ultrason (IVUS)

Le système circulatoire -- Les maladies cardiovasculaires -- L'imagerie vasculaire -- Les techniques de segmentatin d'images -- Segmentation d'images IVUS -- Méthodologie -- Protocole d'acquisiton des données -- Prétraitement des images -- Sélection des caractéristiques -- Extraction des caractéristiques à partir des images IVUS -- Réduction de dimensionnalité par analyse par composantes principales -- Classification des caractéristiques par un classificateur k-moyen -- Le post traitement -- Méthode de validation -- Description de la base de données utilisée -- Choix de la taille des cubes et du rayon -- Choix de la meilleure combinaison -- Analyse des paramètres -- Évaluation de la méthode sur des images réelles de patients

Quantifying Atherosclerosis: IVUS Imaging For Lumen Border Detection And Plaque Characterization

The importance of atherosclerotic disease in coronary artery has been a subject of study for many researchers in the past decade. In brief, the aim is to understand progression of such a disease, detect plaques at risks (vulnerable plaques), and treat them selectively to prevent mortality and immobility. Consequently, several imaging modalities have been developed and among them intravascular ultrasound (IVUS) has been of particular interest since it provides useful information about tissues microstructures and images with sufficient penetration as well as resolution. In general, the ultimate goal is to provide interventional cardiologists with reliable clinical tools so they can identify vulnerable plaques, make decisions confidently, choose the most appropriate drugs or implant devices (i.e. stent), and stabilize them during catheterization procedures with minimal risk. In this work, we review existing atherosclerotic tissue characterization algorithms including the state-of-the-art virtual histology (VH) framework, which has been implemented in the Volcano (Rancho Cordova, CA) IVUS clinical scanners using 64-elements 20 MHz phased-array transducer. Initially, we intended to extend this technique for data acquired with 40 MHz single-element transducers. For this reason, we started acquiring in vitro IVUS data and studied involved challenges from specimen preparation toward classification. We observed inconsistency among extracted features along with transducer's spectral parameters (i.e. bandwidth, center frequency). This, in addition to infeasibility of construction of reliable training dataset due to heterogeneity of atherosclerotic tissues motivated us to develop an unsupervised texture-based atherosclerotic tissue characterization algorithm. We proposed a two-dimensional multiscale wavelet-based algorithm to expand IVUS backscattered signals and/or grayscale images onto orthogonal symmetric quadrature mirror filters (QMF) such as Lemarie-Battle. At the bottom of decomposition tree, we employed ISODATA to cluster enveloped detected features in an unsupervised fashion and classify atherosclerotic plaque constitutes into fibrotic, lipidic, calcified, and no tissues. For the first time, we studied numbers of factors that were necessary for extension of in vitro derived classifier for in vivo applications such as reliability of classified tissues behind arc of calcified plaques and effects of pressure changes as well as flowing blood on constructed tissue color maps, called prognosis histology (PH) images. The second half of this dissertation is devoted to automatic detection of lumen borders in IVUS grayscale images acquired with high frequency (40 MHz up) transducers where more scattering exhibited within lumen area that makes the problem of interest more challenging. We established our framework on three-dimensional expansion of IVUS sub-volumes onto orthonormal brushlet basis function. The rational behind our framework was presence of incoherent (i.e. blood) versus coherent (i.e. plaque, surrounding fat) textural patterns along pullback direction, which was motivated by what an interventional cardiologist does to locate the lumen border visually by going back and forth among IVUS frames. We studied the feasibility of brushlet analysis through filtering blood speckles and supervised classification of blood versus non-blood regions. Our preliminary study confirmed that the most informative features reside in the innermost cubes, representing low-frequency components in transformed domain. Finally, we explored that tissue responses to IVUS signals are proportionally preserved in brushlet coefficients and it enabled us to classify blood regions in complex brushlet space. Subsequently, we employed surface function actives (SFA) to estimate the lumen borders after regularization. In a comparison study, we quantified our results with two of existing algorithms, employing IVUS grayscale images acquired with 40 MHz and 45 MHz single-element transducers. Overall, our proposed algorithm outperformed and the resulting automated detected borders showed good correlation with manually traced borders by an expert

Klassifikation morphologischer und pathologischer Strukturen in koronaren Gefäßen auf Basis intravaskulärer Ultraschallaufnahmen zur klinischen Anwendung in einem IVB-System

Erkrankungen des Herz-Kreislaufsystems sind in Deutschland für fast 50% der Todesfälle verantwortlich. Insbesondere die Arteriosklerose (vulgo: „Arterienverkalkung“) ist dabei ein dominierendes Krankheitsbild. So ist es auch nicht verwunderlich, dass die Arteriosklerose seit den Anfängen der wissenschaftlichen Medizin ein Feld für umfangreiche Untersuchungen gewesen ist. Speziell durch den technischen Fortschritt bildgebender Verfahren war es möglich neuartige Diagnose- und Therapiemethoden zu entwickeln. Dabei hat sich gerade der intravaskuläre Ultraschall zu einem Goldstandard in der Diagnose arteriosklerotischer Erkrankungen und, in Kombination mit der intravaskulären Brachytherapie, zu einer Erfolg versprechenden Basistechnik für therapeutische Maßnahmen entwickelt. Grundvoraussetzung fast jeder bildbasierten Intervention ist aber die Separierung der Bilddaten in anatomisch und pathologisch differenzierte, saliente Regionen. In Anbetracht zunehmender, umfangreicherer Datenmengen kann eine derartige Aufarbeitung nur rechnergestützt durch Problem adaptierte Klassifikationsalgorithmen gewährleistet werden. Daher war es das Ziel dieser Arbeit, neue Methoden zur Merkmalsextraktion und Algorithmen zur Klassifikation morphologischer und pathologischer Strukturen in koronaren Gefäßen bereitzustellen. Aus der initialen Fragestellung wurde zudem zeitnah deutlich, dass das Forschungsvorhaben Anknüpfungspunkte zu weiteren hochgradig relevanten inter- und intradisziplinären Forschungsthemen, beispielsweise der Histologie, Systembiologie oder Chemietechnik, aufweist. Aber auch vonseiten der Anwendungsszenarien wurden teilweise völlig neue, innovative Wege beschritten. Exemplarisch sei ein E-Learning-Ansatz zur „Übersetzung“ digitaler Bilddaten in haptisch erfahrbare Reliefs für blinde und sehbehinderte Schülerinnen und Schüler genannt. In Anbetracht dieser partiell divergierenden Sichtweisen war auch die generalisierte, von der expliziten Fragestellung abstrahierte Umsetzung eine Ausrichtung der Arbeit. Dieser Intention folgend wurden drei wesentliche methodische und konzeptionelle Entwicklungen innerhalb der Arbeit realisiert: ein Expertensystem zur Approximation arterieller Kompartimente mittels unscharfer elliptischer Templates, ein neuartiger, effizienter Ansatz zur signaltheoretischen Extraktion textureller Merkmale und die Etablierung maschinelle Lernverfahren unter Integration von a priori Wissen. Über eine konsequente Integration statistischer Gütemaße konnte zudem eine ausgeprägte Rückkopplung zwischen Klassifikations- und Bewertungsansätzen gewährleistet werden. Gemeinsam ist allen Ansätzen das Ansinnen, trotz hoch anwendungsbezogener Umsetzungen, die fortwährende Portabilität zu beachten. In einer übergeordneten Abstraktion kann die Intention der Arbeit somit auch in der „generalisierten Nutzung signaltheoretischer Merkmale zur Klassifikation heterogener, durch texturelle Ausprägungen zu differenzierende Kompartimente mittels maschineller Lernverfahren“ verstanden werden