Automated quantification of cartilage quality for hip treatment decision support

Purpose Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools. Methods A system for the efficient automatic parametrization of the 3D hip cartilage was developed. 2D U-nets were trained on manually annotated dual-flip angle (DFA) dGEMRIC for femoral head localization and cartilage segmentation. A fully automated cartilage sectioning pipeline for analysis of central and peripheral regions, femoral-acetabular layers, and a variable number of section slices, was developed along with functionality for the automatic calculation of dGEMRIC index, thickness, surface area, and volume. Results The trained networks locate the femoral head and segment the cartilage with a Dice similarity coefficient of 88 ± 3 and 83 ± 4% on DFA and magnetization-prepared 2 rapid gradient-echo (MP2RAGE) dGEMRIC, respectively. A completely automatic cartilage analysis was performed in 18s, and no significant difference for average dGEMRIC index, volume, surface area, and thickness calculated on manual and automatic segmentation was observed. Conclusion An application for the 3D analysis of hip cartilage was developed for the automated detection of subtle morphological and biochemical signs of cartilage degradation in prognostic studies and clinical diagnosis. The segmentation network achieved a 4-time increase in processing speed without loss of segmentation accuracy on both normal and deformed anatomy, enabling accurate parametrization. Retraining of the networks with the promising MP2RAGE protocol would enable analysis without the need for B1 inhomogeneity correction in the future

Fusion and Analysis of Multidimensional Medical Image Data

Analýza medicínských obrazů je předmětem základního výzkumu již řadu let. Za tu dobu bylo v této oblasti publikováno mnoho výzkumných prací zabývajících se dílčími částmi jako je rekonstrukce obrazů, restaurace, segmentace, klasifikace, registrace (lícování) a fúze. Kromě obecného úvodu, pojednává tato disertační práce o dvou medicínsky orientovaných tématech, jež byla formulována ve spolupráci s Philips Netherland BV, divizí Philips Healthcare. První téma je zaměřeno na oblast zpracování obrazů subtrakční angiografie dolních končetin člověka získaných pomocí výpočetní X-Ray tomografie (CT). Subtrakční angiografie je obvykle využívaná při podezření na periferní cévní onemocnění (PAOD) nebo při akutním poškození dolních končetin jako jsou fraktury apod. Současné komerční metody nejsou dostatečně spolehlivé už v předzpracování, jako je například odstranění pacientského stolu, pokrývky, dlahy, apod. Spolehlivost a přesnost identifikace cév v subtrahovaných datech vedoucích v blízkosti kostí je v důsledku Partial Volume artefaktu rovněž nízká. Automatické odstranění kalcifikací nebo detekce malých cév doplňujících nezbytnou informaci o náhradním zásobení dolních končetin krví v případě přerušení hlavních zásobujících cév v současné době rovněž nesplňují kritéria pro plně automatické zpracování. Proto hlavním cílem týkající se tohoto tématu bylo vyvinout automatický systém, který by mohl současné nedostatky v CTSA vyšetření odstranit. Druhé téma je orientováno na identifikaci patologických změn na páteři člověka v CT obrazech se zaměřením na osteolytické a osteoblastické léze u jednotlivých obratlů. Tyto změny obvykle nastávají v důsledků postižení metastazujícím procesem rakovinového onemocnění. Pro detekci patologických změn je pak potřeba identifikace a segmentace jednotlivých obratlů. Přesnost analýzy jednotlivých lézí však závisí rovněž na správné identifikaci těla a zadních segmentů u jednotlivých obratlů a na segmentaci trabekulárního centra obratlů, tj. odstranění kortikální kosti. Během léčby mohou být pacienti skenováni vícekrát, obvykle s několika-mesíčním odstupem. Hodnocení případného vývoje již detekovaných patologických změn pak logicky vychází ze správné detekce patologií v jednotlivých obratlech korespondujících si v jednotlivých akvizicích. Jelikož jsou příslušné obratle v jednotlivých akvizicích obvykle na různé pozici, jejich fúze, vedoucí k analýze časového vývoje detekovaných patologií, je komplikovaná. Požadovaným výsledkem v tomto tématu je vytvoření komplexního systému pro detekci patologických změn v páteři, především osteoblastických a osteolytických lézí. Takový systém tedy musí umožnovat jak segmentaci jednotlivých obratlů, jejich automatické rozdělení na hlavní části a odstranění kortikální kosti, tak také detekci patologických změn a jejich hodnocení. Ačkoliv je tato disertační práce v obou výše zmíněných tématech primárně zaměřena na experimentální část zpracování medicínských obrazů, zabývá se všemi nezbytnými kroky, jako je předzpracování, registrace, dodatečné zpracování a hodnocení výsledků, vedoucími k možné aplikovatelnosti obou systému v klinické praxi. Jelikož oba systémy byly řešeny v rámci týmové spolupráce jako celek, u obou témat jsou pro některé konkrétní kroky uvedeny odkazy na doktorskou práci Miloše Malínského.Analysis of medical images has been subject of basic research for many years. Many research papers have been published in the field related to image analysis and focused on partial aspects such as reconstruction, restoration, segmentation and classification, registration (spatial alignment) and fusion. Besides the introduction of related general concepts used in medical image processing, this thesis deals with two specific medical problems formulated in cooperation with Philips Netherland BV, Philips Healthcare division. The first topic is focused on subtraction angiography in patients’ lower legs utilizing image data from X-Ray computed tomography (CT). CT subtraction angiography (CTSA) is typically used for indication of the Peripheral Artery Occlusive Disease (PAOD) and for examination of acute injuries of lower legs such as acute fractures, etc. Current methods in clinical praxis are not sufficient regarding the pre-processing such as masking of patient desk, cover, splint, etc. The subtraction of blood vessels adjacent to neighboring bones in lower legs is of low accuracy due to the Partial Volume artifact. Masking of calcifications and detection of tiny blood vessels complementing necessary information about the alternative blood supply in lower legs in case of obstruction in main arteries is also not reliable for fully automated process presently. Therefore, the main aim regarding this topic was to develop an automated framework that could overcome current shortcomings in CTSA examination. The second topic is oriented on the identification and evaluation of pathologic changes in human spine, focusing on osteolytic and osteoblastic lesions in individual vertebrae in CT images. Such changes occur typically as a consequence of metastasizing process of cancerous disease. For the detection of pathologic changes, an identification and segmentation of individual vertebrae is necessary. Moreover, the analysis of individual lesions in vertebrae depends also on correct identification of vertebral body and posterior segments of each vertebra, and on segmentation of their trabecular centers. Patients are typically examined more than once during their therapy. Then, the evaluation of possible tumorous progression is based on accurate detection of pathologies in individual vertebrae in the base-line and corresponding follow-up images. Since the corresponding vertebrae are in mutually different positions in the follow-up images, their fusion leading to the analysis of the lesion progression is complicated. The main aim regarding this topic is to develop a complex framework for detection of pathologic lesions on spine, with the main focus on osteoblastic and osteolystic lesions. Such system has to provide not only reliable segmentation of individual vertebrae and detection of their main regions but also the masking of their cortical bone, detection of their pathologic changes and their evaluation. Although this dissertation thesis is primarily oriented at the experimental part of medical image processing considering both the above mentioned topics, it deals with all necessary processing steps, i.e. preprocessing, image registration, post-processing and evaluation of results, leading to the future use of both frameworks in clinical practice. Since both frameworks were developed in a team, there are some chapters referring to the dissertation thesis of Milos Malinsky.

Automatic Mesh-Based Segmentation of Multiple Organs in MR Images

La segmentation de structures anatomiques multiples dans des images de résonance magnétique (RM) est souvent requise dans des applications de génie biomédical telles que la simulation numérique, la chirurgie guidée par l’image, la planification de traitements, etc. De plus, il y a un besoin croissant pour une segmentation automatique d’organes multiples et de structures complexes à partir de cette modalité d’imagerie. Il existe plusieurs techniques de segmentation multi-objets qui ont été appliquées avec succès sur des images de tomographie axiale à rayons-X (CT). Cependant, dans le cas des images RM cette tâche est plus difficile en raison de l’inhomogénéité des intensités dans ces images et de la variabilité dans l’apparence des structures anatomiques. Par conséquent, l’état de l’art sur la segmentation multi-objets sur des images RM est beaucoup plus faible que celui sur les images CT. Parmi les travaux qui portent sur la segmentation d’images RM, les approches basées sur la segmentation de régions sont sensibles au bruit et la non uniformité de l’intensité dans les images. Les approches basées sur les contours ont de la difficulté à regrouper les informations sur les contours de sorte à produire un contour fermé cohérent. Les techniques basées sur les atlas peuvent avoir des problèmes en présence de structures complexes avec une grande variabilité anatomique. Les modèles déformables représentent une des méthodes les plus populaire pour la détection automatique de différents organes dans les images RM. Cependant, ces modèles souffrent encore d’une limitation importante qui est leur sensibilité à la position initiale et la forme du modèle. Une initialisation inappropriée peut conduire à un échec dans l’extraction des frontières des objets. D’un autre côté, le but ultime d’une segmentation automatique multi-objets dans les images RM est de produire un modèle qui peut aider à extraire les caractéristiques structurelles d’organes distincts dans les images. Les méthodes d’initialisation automatique actuelles qui utilisent différents descripteurs ne réussissent pas complètement l’extraction d’objets multiples dans les images RM. Nous avons besoin d’exploiter une information plus riche qui se trouve dans les contours des organes. Dans ce contexte les maillages adaptatifs anisotropiques semblent être une solution potentielle au problème soulevé. Les maillages adaptatifs anisotropiques construits à partir des images RM contiennent de l’information à un plus haut niveau d’abstraction représentant les éléments, d’une orientation et d’une forme donnée, qui constituent les différents organes dans l’image. Les méthodes existantes pour la construction de maillages adaptatifs sont basées sur les intensités dans l’image et possèdent une limitation pratique qui est l’alignement inadéquat des éléments du maillage en présence de contours inclinés dans l’image. Par conséquent, nous avons aussi besoin d’améliorer le processus d’adaptation de maillage pour produire une meilleure représentation de l’image basée sur un maillage.----------ABSTRACT: Segmentation of multiple anatomical structures in MR images is often required for biomedical engineering applications such as clinical simulation, image-guided surgery, treatment planning, etc. Moreover, there is a growing need for automatic segmentation of multiple organs and complex structures from this medical imaging modality. Many successful multi-object segmentation attempts were introduced for CT images. However in the case of MR images it is a more challenging task due to intensity inhomogeneity and variability of anatomy appearance. Therefore, state-of-the-art in multi-object MR segmentation is very inferior to that of CT images. In literature dealing with MR image segmentation, the region-based approaches are sensitive to noise and non-uniformity in the input image. The edge-based approaches are challenging to group the edge information into a coherent closed contour. The atlas-based techniques can be problematic for complicated structures with anatomical variability. Deformable models are among the most popular methods for automatic detection of different organs in MR images. However they still have an important limitation which is that they are sensitive to initial position and shape of the model. An unsuitable initialization may provide failure to capture the true boundaries of the objects. On the other hand, a useful aim for an automatic multi-object MR segmentation is to provide a model which promotes understanding of the structural features of the distinct objects within the MR images. The current automatic initialization methods which have used different descriptors are not completely successful in extracting multiple objects from MR images and we need to find richer information that is available from edges. In this regard, anisotropic adaptive meshes seem to be a potential solution to the aforesaid limitation. Anisotropic adaptive meshes constructed from MR images contain higher level, abstract information about the anatomical structures of the organs within the image retained as the elements shape and orientation. Existing methods for constructing adaptive meshes based on image features have a practical limitation where manifest itself in inadequate mesh elements alignment to inclined edges in the image. Therefore, we also have to enhance mesh adaptation process to provide a better mesh-based representation. In this Ph.D. project, considering the highlighted limitations we are going to present a novel method for automatic segmentation of multiple organs in MR images by incorporating mesh adaptation techniques. In our progress, first, we improve an anisotropic adaptation process for the meshes that are constructed from MR images where the mesh elements align adequately to the image content and improve mesh anisotropy along edges in all directions. Then the resulting adaptive meshes are used for initialization of multiple active models which leads to extract initial object boundaries close to the true boundaries of multiple objects simultaneously. Finally, the Vector Field Convolution method is utilized to guide curve evolution towards the object boundaries to obtain the final segmentation results and present a better performance in terms of speed and accuracy

Efficient automatic correction and segmentation based 3D visualization of magnetic resonance images

In the recent years, the demand for automated processing techniques for digital medical image volumes has increased substantially. Existing algorithms, however, still often require manual interaction, and newly developed automated techniques are often intended for a narrow segment of processing needs. The goal of this research was to develop algorithms suitable for fast and effective correction and advanced visualization of digital MR image volumes with minimal human operator interaction. This research has resulted in a number of techniques for automated processing of MR image volumes, including a novel MR inhomogeneity correction algorithm derivative surface fitting (dsf), automatic tissue detection algorithm (atd), and a new fast technique for interactive 3D visualization of segmented volumes called gravitational shading (gs). These newly developed algorithms provided a foundation for the automated MR processing pipeline incorporated into the UniViewer medical imaging software developed in our group and available to the public. This allowed the extensive testing and evaluation of the proposed techniques. Dsf was compared with two previously published methods on 17 digital image volumes. Dsf demonstrated faster correction speeds and uniform image quality improvement in this comparison. Dsf was the only algorithm that did not remove anatomic detail. Gs was compared with the previously published algorithm fsvr and produced rendering quality improvement while preserving real-time frame-rates. These results show that the automated pipeline design principles used in this dissertation provide necessary tools for development of a fast and effective system for the automated correction and visualization of digital MR image volumes

Doctor of Philosophy

dissertationAltered mechanics are believed to initiate osteoarthritis in hips with acetabular dysplasia. Periacetabular osteotomy (PAO) is the preferred surgical treatment; however, it is unknown if the procedure normalizes joint anatomy and mechanics. Changes in three-dimensional (3D) morphology and chondrolabral mechanics were quantified after PAO. Finite element (FE) models demonstrated that PAO improved the distribution of coverage, reduced stress, increased congruity, and prevented cartilage thinning. However, changes in mechanics were not consistent. In fact, one patient exhibited increased stress after surgery, which was believed to be a result of over-correction. Therefore, methods to integrate morphologic and biomechanical analysis with clinical care could standardize outcomes of PAO. FE simulations are time-intensive and require significant computing resources. Therefore, the second aim was to implement an efficient method to estimate mechanics. An enhanced discrete element analysis (DEA) model of the hip that accurately incorporated cartilage geometry and efficiently calculated stress was developed and analyzed. Although DEA model estimates predicted elevated magnitudes of contact stress, the distribution corresponded well with FE models. As a computationally efficient platform, DEA could assist in diagnosis and surgical planning. Imaging is a precursor to analyzing morphology and biomechanics. Ideally, an imaging protocol would visualize bone and soft-tissue at high resolution without ionizing radiation. Magnetic resonance imaging (MRI) with 3D dual-echo-steady-state (DESS) is a promising sequence to image the hip noninvasively, but its accuracy has not been quantified. Therefore, the final aim was to implement and validate the use of 3D DESS MRI in the hip. Using direct measurements of cartilage thickness as the standard, 3D DESS MRI imaged cartilage to ~0.5 mm of the physical measurements with 95% confidence, which is comparable to the most accurate hip imaging protocol presented to date. In summary, this dissertation provided unique insights into the morphologic and biomechanical features following PAO. In the future, DEA could be combined with 3D DESS MRI to efficiently analyze contact stress distributions. These methods could be incorporated into preoperative planning software, where the algorithm would predict the optimal relocation of the acetabulum to maximize femoral head coverage while minimizing contact stress, and thereby improve long-term outcomes of PAO