Retrospective study on phantom for the application of medical image registration in the operating room scenario

This paper presents a phantom study to asses the feasibility of the medical image registration algorithms in the operating room (OR) scenario. The main issues of the registration algorithms in an OR application are, on one hand, the lack of the initial guess of the registration transformation - the images to be registered may be completely independentand, on the other hand, the multimodality of the data. Other requirements to be addressed by the OR registration algorithms are: real-time execution and the necessity of the validation of the results. This work analyzes how, under these requirements, the current state of the art algorithms in medical image registration may be used and shows which direction should be taken when designing a OR navigation system that includes registration as a component

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach

In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated. In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data. Next, a manifold learning-based scale invariant global shape descriptor is introduced. The proposed descriptor benefits from the capability of Laplacian Eigenmap in dealing with high dimensional data by introducing an exponential weighting scheme. It eliminates the limitations tied to the well-known cotangent weighting scheme, namely dependency on triangular mesh representation and high intra-class quality of 3D models. In the end, a novel descriptive model for diagnostic classification of pulmonary nodules is presented. The descriptive model benefits from structural differences between benign and malignant nodules for automatic and accurate prediction of a candidate nodule. It extracts concise and discriminative features automatically from the 3D surface structure of a nodule using spectral features studied in the previous work combined with a point cloud-based deep learning network. Extensive experiments have been conducted and have shown that the proposed algorithms based on manifold learning outperform several state-of-the-art methods. Advanced computational techniques with a combination of manifold learning and deep networks can play a vital role in effective healthcare delivery by providing a framework for several fundamental tasks in image and shape processing, namely, registration, classification, and detection of features of interest

Towards Robust and Accurate Image Registration by Incorporating Anatomical and Appearance Priors

Ph.DDOCTOR OF PHILOSOPH

Multi-Modal Similarity Learning for 3D Deformable Registration of Medical Images

Alors que la perspective de la fusion d images médicales capturées par des systèmes d imageries de type différent est largement contemplée, la mise en pratique est toujours victime d un obstacle théorique : la définition d une mesure de similarité entre les images. Des efforts dans le domaine ont rencontrés un certain succès pour certains types d images, cependant la définition d un critère de similarité entre les images quelle que soit leur origine et un des plus gros défis en recalage d images déformables. Dans cette thèse, nous avons décidé de développer une approche générique pour la comparaison de deux types de modalités donnés. Les récentes avancées en apprentissage statistique (Machine Learning) nous ont permis de développer des solutions innovantes pour la résolution de ce problème complexe. Pour appréhender le problème de la comparaison de données incommensurables, nous avons choisi de le regarder comme un problème de plongement de données : chacun des jeux de données est plongé dans un espace commun dans lequel les comparaisons sont possibles. A ces fins, nous avons exploré la projection d un espace de données image sur l espace de données lié à la seconde image et aussi la projection des deux espaces de données dans un troisième espace commun dans lequel les calculs sont conduits. Ceci a été entrepris grâce à l étude des correspondances entre les images dans une base de données images pré-alignées. Dans la poursuite de ces buts, de nouvelles méthodes ont été développées que ce soit pour la régression d images ou pour l apprentissage de métrique multimodale. Les similarités apprises résultantes sont alors incorporées dans une méthode plus globale de recalage basée sur l optimisation discrète qui diminue le besoin d un critère différentiable pour la recherche de solution. Enfin nous explorons une méthode qui permet d éviter le besoin d une base de données pré-alignées en demandant seulement des données annotées (segmentations) par un spécialiste. De nombreuses expériences sont conduites sur deux bases de données complexes (Images d IRM pré-alignées et Images TEP/Scanner) dans le but de justifier les directions prises par nos approches.Even though the prospect of fusing images issued by different medical imagery systems is highly contemplated, the practical instantiation of it is subject to a theoretical hurdle: the definition of a similarity between images. Efforts in this field have proved successful for select pairs of images; however defining a suitable similarity between images regardless of their origin is one of the biggest challenges in deformable registration. In this thesis, we chose to develop generic approaches that allow the comparison of any two given modality. The recent advances in Machine Learning permitted us to provide innovative solutions to this very challenging problem. To tackle the problem of comparing incommensurable data we chose to view it as a data embedding problem where one embeds all the data in a common space in which comparison is possible. To this end, we explored the projection of one image space onto the image space of the other as well as the projection of both image spaces onto a common image space in which the comparison calculations are conducted. This was done by the study of the correspondences between image features in a pre-aligned dataset. In the pursuit of these goals, new methods for image regression as well as multi-modal metric learning methods were developed. The resulting learned similarities are then incorporated into a discrete optimization framework that mitigates the need for a differentiable criterion. Lastly we investigate on a new method that discards the constraint of a database of images that are pre-aligned, only requiring data annotated (segmented) by a physician. Experiments are conducted on two challenging medical images data-sets (Pre-Aligned MRI images and PET/CT images) to justify the benefits of our approach.CHATENAY MALABRY-Ecole centrale (920192301) / SudocSudocFranceF

Latent Disentanglement for the Analysis and Generation of Digital Human Shapes

Analysing and generating digital human shapes is crucial for a wide variety of applications ranging from movie production to healthcare. The most common approaches for the analysis and generation of digital human shapes involve the creation of statistical shape models. At the heart of these techniques is the definition of a mapping between shapes and a low-dimensional representation. However, making these representations interpretable is still an open challenge. This thesis explores latent disentanglement as a powerful technique to make the latent space of geometric deep learning based statistical shape models more structured and interpretable. In particular, it introduces two novel techniques to disentangle the latent representation of variational autoencoders and generative adversarial networks with respect to the local shape attributes characterising the identity of the generated body and head meshes. This work was inspired by a shape completion framework that was proposed as a viable alternative to intraoperative registration in minimally invasive surgery of the liver. In addition, one of these methods for latent disentanglement was also applied to plastic surgery, where it was shown to improve the diagnosis of craniofacial syndromes and aid surgical planning

Hand eye coordination in surgery

The coordination of the hand in response to visual target selection has always been regarded as an essential quality in a range of professional activities. This quality has thus far been elusive to objective scientific measurements, and is usually engulfed in the overall performance of the individuals. Parallels can be drawn to surgery, especially Minimally Invasive Surgery (MIS), where the physical constraints imposed by the arrangements of the instruments and visualisation methods require certain coordination skills that are unprecedented. With the current paradigm shift towards early specialisation in surgical training and shortened focused training time, selection process should identify trainees with the highest potentials in certain specific skills. Although significant effort has been made in objective assessment of surgical skills, it is only currently possible to measure surgeons’ abilities at the time of assessment. It has been particularly difficult to quantify specific details of hand-eye coordination and assess innate ability of future skills development. The purpose of this thesis is to examine hand-eye coordination in laboratory-based simulations, with a particular emphasis on details that are important to MIS. In order to understand the challenges of visuomotor coordination, movement trajectory errors have been used to provide an insight into the innate coordinate mapping of the brain. In MIS, novel spatial transformations, due to a combination of distorted endoscopic image projections and the “fulcrum” effect of the instruments, accentuate movement generation errors. Obvious differences in the quality of movement trajectories have been observed between novices and experts in MIS, however, this is difficult to measure quantitatively. A Hidden Markov Model (HMM) is used in this thesis to reveal the underlying characteristic movement details of a particular MIS manoeuvre and how such features are exaggerated by the introduction of rotation in the endoscopic camera. The proposed method has demonstrated the feasibility of measuring movement trajectory quality by machine learning techniques without prior arbitrary classification of expertise. Experimental results have highlighted these changes in novice laparoscopic surgeons, even after a short period of training. The intricate relationship between the hands and the eyes changes when learning a skilled visuomotor task has been previously studied. Reactive eye movement, when visual input is used primarily as a feedback mechanism for error correction, implies difficulties in hand-eye coordination. As the brain learns to adapt to this new coordinate map, eye movements then become predictive of the action generated. The concept of measuring this spatiotemporal relationship is introduced as a measure of hand-eye coordination in MIS, by comparing the Target Distance Function (TDF) between the eye fixation and the instrument tip position on the laparoscopic screen. Further validation of this concept using high fidelity experimental tasks is presented, where higher cognitive influence and multiple target selection increase the complexity of the data analysis. To this end, Granger-causality is presented as a measure of the predictability of the instrument movement with the eye fixation pattern. Partial Directed Coherence (PDC), a frequency-domain variation of Granger-causality, is used for the first time to measure hand-eye coordination. Experimental results are used to establish the strengths and potential pitfalls of the technique. To further enhance the accuracy of this measurement, a modified Jensen-Shannon Divergence (JSD) measure has been developed for enhancing the signal matching algorithm and trajectory segmentations. The proposed framework incorporates high frequency noise filtering, which represents non-purposeful hand and eye movements. The accuracy of the technique has been demonstrated by quantitative measurement of multiple laparoscopic tasks by expert and novice surgeons. Experimental results supporting visual search behavioural theory are presented, as this underpins the target selection process immediately prior to visual motor action generation. The effects of specialisation and experience on visual search patterns are also examined. Finally, pilot results from functional brain imaging are presented, where the Posterior Parietal Cortical (PPC) activation is measured using optical spectroscopy techniques. PPC has been demonstrated to involve in the calculation of the coordinate transformations between the visual and motor systems, which establishes the possibilities of exciting future studies in hand-eye coordination

Deformable Medical Image Registration: A Survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this technical report, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this technical report is to provide an extensive account of registration techniques in a systematic manner.Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des différents types de modalités a n de faciliter le diagnostic et la planification du traitement; ii) les études longitudinales, oú des changements structurels ou anatomiques sont étudiées en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population et les atlas statistiques. Dans ce rapport de recherche, nous essayons de donner un aperçu des différentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particulièrement insisté sur les techniques appliquées aux images médicales. A n d'étudier les méthodes du recalage d'images, leurs composants principales sont d'abord identifiés puis étudiées de manière indépendante, les techniques les plus récentes étant classifiées en suivant un schéma logique déterminé. La contribution de ce rapport de recherche est de fournir un compte rendu détaillé des techniques de recalage d'une manière systématique

Registration of medical images for applications in minimally invasive procedures

Il punto di partenza di questa tesi \ue8 l'analisi dei metodi allo stato dell'arte di registrazione delle immagini mediche per verificare se sono adatti ad essere utilizzati per assistere il medico durante una procedura minimamente invasiva , ad esempio una procedura percutanea eseguita manualmente o un intervento teleoperato eseguito per mezzo di un robot . La prima conclusione \ue8 che, anche se ci sono tanti lavori dedicati allo sviluppo di algoritmi di registrazione da applicare nel contesto medico, la maggior parte di essi non sono stati progettati per essere utilizzati nello scenario della sala operatoria (OR) anche perch\ue9, rispetto ad altre applicazioni , OR richiede anche la validazione, prestazioni in tempo reale e la presenza di altri strumenti . Gli algoritmi allo stato dell'arte sono basati su un iterazione in tre fasi : ottimizzazione - trasformazione - valutazione della somiglianza delle immagini registrate. In questa tesi, studiamo la fattibilit\ue0 dell'approccio in tre fasi per applicazioni OR, mostrando i limiti che tale approccio incontra nelle applicazioni che stiamo considerando. Verr\ue0 dimostrato come un metodo semplice si potrebbe utilizzare nella OR. Abbiamo poi sviluppato una teoria che \ue8 adatta a registrare grandi insiemi di dati non strutturati estratti da immagini mediche, tenendo conto dei vincoli della OR . Vista l'impossibilit\ue0 di lavorare con dati medici di tipo DICOM, verr\ue0 impiegato un metodo per registrare dataset composti da insiemi di punti non strutturati. Gli algoritmi proposti sono progettati per trovare la corrispondenza spaziale in forma chiusa tenendo conto del tipo di dati, il vincolo del tempo e la presenza di rumore e /o piccole deformazioni. La teoria e gli algoritmi che abbiamo sviluppato sono derivati dalla teoria delle forme proposta da Kendall (Kendall's shapes) e utilizza un descrittore globale della forma per calcolare le corrispondenze e la distanza tra le strutture coinvolte . Poich\ue9 la registrazione \ue8 solo una componente nelle applicazioni mediche, l' ultima parte della tesi \ue8 dedicata ad alcune applicazioni pratiche in OR che possono beneficiare della procedura di registrazione .The registration of medical images is necessary to establish spatial correspondences across two or more images. Registration is rarely the end-goal, but instead, the results of image registration are used in other tasks. The starting point of this thesis is to analyze which methods at the state of the art of image registration are suitable to be used in assisting a physician during a minimally invasive procedure, such as a percutaneous procedure performed manually or a teleoperated intervention performed by the means of a robot. The first conclusion is that, even if much previous work has been devoted to develop registration algorithms to be applied in the medical context, most of them are not designed to be used in the operating room scenario (OR) because, compared to other applications, the OR requires also a strong validation, real-time performance and the presence of other instruments. Almost all of these algorithms are based on a three phase iteration: optimize-transform-evaluate similarity. In this thesis, we study the feasibility of this three steps approach in the OR, showing the limits that such approach encounter in the applications we are considering. We investigate how could a simple method be realizable and what are the assumptions for such a method to work. We then develop a theory that is suitable to register large sets of unstructured data extracted from medical images keeping into account the constraints of the OR. The use of the whole radiologic information is not feasible in the OR context, therefore the method we are introducing registers processed dataset extracted from the original medical images. The framework we propose is designed to find the spatial correspondence in closed form keeping into account the type of the data, the real-time constraint and the presence of noise and/or small deformations. The theory and algorithms we have developed are in the framework of the shape theory proposed by Kendall (Kendall's shapes) and uses a global descriptor of the shape to compute the correspondences and the distance between shapes. Since the registration is only a component of a medical application, the last part of the thesis is dedicated to some practical applications in the OR that can benefit from the registration procedure