Convolutional neuronal networks combined with X-ray phase-contrast imaging for a fast and observer-independent discrimination of cartilage and liver diseases stages

We applied transfer learning using Convolutional Neuronal Networks to high resolution X-ray phase contrast computed tomography datasets and tested the potential of the systems to accurately classify Computed Tomography images of different stages of two diseases, i.e. osteoarthritis and liver fibrosis. The purpose is to identify a time-effective and observer-independent methodology to identify pathological conditions. Propagation-based X-ray phase contrast imaging WAS used with polychromatic X-rays to obtain a 3D visualization of 4 human cartilage plugs and 6 rat liver samples with a voxel size of 0.7x0.7x0.7 mu m(3) and 2.2x2.2x2.2 mu m(3), respectively. Images with a size of 224x224 pixels are used to train three pre-trained convolutional neuronal networks for data classification, which are the VGG16, the Inception V3, and the Xception networks. We evaluated the performance of the three systems in terms of classification accuracy and studied the effect of the variation of the number of inputs, training images and of iterations. The VGG16 network provides the highest classification accuracy when the training and the validation-test of the network are performed using data from the same samples for both the cartilage (99.8%) and the liver (95.5%) datasets. The Inception V3 and Xception networks achieve an accuracy of 84.7% (43.1%) and of 72.6% (53.7%), respectively, for the cartilage (liver) images. By using data from different samples for the training and validation-test processes, the Xception network provided the highest test accuracy for the cartilage dataset (75.7%), while for the liver dataset the VGG16 network gave the best results (75.4%). By using convolutional neuronal networks we show that it is possible to classify large datasets of biomedical images in less than 25 min on a 8 CPU processor machine providing a precise, robust, fast and observer-independent method for the discrimination/classification of different stages of osteoarthritis and liver diseases

Propagation phase-contrast micro-computed tomography allows laboratory-based three-dimensional imaging of articular cartilage down to the cellular level

High-resolution non-invasive three-dimensional (3D) imaging of chondrocytes in articular cartilage remains elusive. The aim of this study was to explore whether laboratory micro-computed tomography (micro-CT) permits imaging cells within articular cartilage

A multi-scale imaging approach to understand osteoarthritis development

X-ray phase-contrast imaging is an innovative and advanced imaging method. Contrary to conventional radiology, where the image contrast is primarily determined by X-ray attenuation, phase-contrast images contain additional information generated by the phase shifts or refraction of the X-rays passing through matter. The refractive effect on tissue samples is orders of magnitude higher than the absorption effect in the X-ray energy range used in biomedical imaging. This technique makes it possible to produce excellent and enhanced image contrast, particularly when examining soft biological tissues or features with similar X-ray attenuation properties. In combination with high spatial resolution detector technology and computer tomography, X-ray phase-contrast imaging has been proved to be a powerful method to examine tissue morphology and the evolution of pathologies three-dimensionally, with great detail and without the need of contrast agents. This Thesis work has focused on developing an accurate, multi-scale X-ray-based methodology for imaging and characterizing the early stages of osteoarthritis. X-ray phase-contrast images acquired at different spatial resolutions provide unprecedented insights into cartilage and the development of its degeneration, i.e., osteoarthritis. Other types of X-ray phase-contrast imaging techniques and setups using spatial resolutions ranging from micrometer down to nanometer were applied. Lower spatial resolutions allow large sample coverage and comprehensive representations, while the nanoscale analysis provides a precise depiction of anatomical details and pathological signs. X-ray phase-contrast results are correlated to data obtained, on the same specimens, by standard laboratory methods, such as histology and transmission electron microscopy. Furthermore, X-ray phase-contrast images of cartilage were acquired using different X-ray sources and results were compared in terms of image quality. It was shown that with the use of synchrotron radiation, more detailed images and much faster data acquisitions could be achieved. A second focus in this Thesis work has been the investigation of the reaction of healthy and degenerated cartilage under different physical pressures, simulating the different levels of stress to which the tissue is subject during daily movements. A specifically designed setup was used to dynamically study cartilage response to varying pressures with X-ray phase-contrast micro-computed tomography, and a fully volumetric and quantitative methodology to accurately describe the tissue morphological variations. This study revealed changes in the behavior of the cartilage cell structure, which differ between normal and osteoarthritic cartilage tissues. The third focus of this Thesis is the realization of an automated evaluation procedure for the discrimination of healthy and cartilage images with osteoarthritis. In recent years, developments in neural networks have shown that they are excellently suited for image classification tasks. The transfer learning method was applied, in which a pre-trained neural network with cartilage images is further trained and then used for classification. This enables a fast, robust and automated grouping of images with pathological findings. A neural network constructed in this way could be used as a supporting instrument in pathology. X-ray phase-contrast imaging computed tomography can provide a powerful tool for a fully 3D, highly accurate and quantitative depiction and characterization of healthy and early stage-osteoarthritic cartilage, supporting the understanding of the development of osteoarthritis.Röntgen-Phasenkontrast-Bildgebung ist eine innovative und weiterführende Bildgebungsmethode. Im Gegensatz zu herkömlichen Absorptions-Röntgenaufnahmen, wie sie in der Radiologie verwendet werden, wird der Kontrast bei dieser Methode aus dem Effekt der Phasenverschiebung oder auch Brechung der Röngtenstrahlen gebildet. Der Brechungseffekt bei Gewebeproben ist um ein Vielfaches höher als der Absorptionseffekt des elektromagnetischen Spektrums der Röntgenstrahlen. Diese Methode ermöglicht die Darstellung von großen Kontraste im Gewebe. Unter Verwendung eines hochauflösenden Detektors und in Kombination mit der Computer-Tomographie, ist Phasenkontrast-Bildgebung eine sehr gute Methode um Knorpelgewebe und Arthrose im Knorpel zu untersuchen. Diese Arbeit beschreibt primär ein Verfahren zur Darstellung arthrotischen Knorpels im Anfangsstadium. Die mit verschiedenen Auflösungen und 3D-Phasen-Kontrast-Methoden produzierten Aufnahmen ermöglichen einen noch nie dagewesenen Einblick in den Knorpel und die Entwicklung von Arthrose im Anfangsstadium. Hierbei kam die propagationsbasierte Phasenkontrastmethode mit einer Auflösung im mikrometer Bereich und die (Nano)-Holotomographie-Methode mit einer Auflösung im Submicrometer Bereich zum Einsatz. Durch Auflösung im mikrometer Bereich kann ein großes Volumen im Knorpel gescannt werden, während die Nano-Holotomographie Methode eine sehr große Detailauflösung aufweißt. Die Phasenkontrast-Aufnahmen werden mit zwei anderen wissenschaftlichen Methoden verglichen: mikroskopische Abbildungen histologisch aufgearbeiteter Knorpelproben und Aufnahmen eines Transmissionselektroskop zeigen sehr große Übereinstimmungen zur Röntgen-Phasenkontrast-Bildgebung. Desweiteren wurden Phasenkontrast-Aufnahmen von Knorpel aus unterschiedlichen Röntgenquellen verglichen. Hierbei zeigte sich, dass mit Hilfe des Teilchenbeschleunigers (Synchrotron) detailreichere und schnellere Aufnahmen erzielt werden können. Bilder aus Flüssig-Metall-Quellen zeigen sich durchaus von guter Qualität, erfordern jedoch sehr lange Aufnahmezeiten. In dieser Arbeit wird zudem das Verhalten von Knorpelgewebe, welches ein Anfangsstadium von Arthrose aufweist, unter physikalischem Druck untersucht. Hierfür wurden 3D-Computertomographie-Aufnahmen von komprimiertem Knorpelgewebe angefertig und mit Aufnahmen ohne Komprimierung verglichen. Ein quantitativer Vergleich machte Veränderungen des Verhaltens der Knorpelzellstruktur (Chondronen) sichtbar. Es konnte gezeigt werden, dass Chondrone bei arthrotischem Knorpel ein verändertes Kompressionsverhalten haben. Der dritte Fokus dieser Arbeit liegt auf der automatisierten Auswertung von Aufnahmen gesunden und arthrotischen Knorpelgewebes. Die Entwicklungen im Bereich der Neuronale Netze zeigten in den letzten Jahren, dass diese sich hervoragend für Bildklassifizierungsaufgaben eignen. Es wurde die Methode des transferierenden Lernens angewandt, bei der ein vortrainiertes Neuronales Netz mit Knorpelbildern weitertrainiert und anschließend zur Klassifizierung eingesetzt wird. Dadurch ist eine schnelle, robuste und automatisierte Gruppierung von Bildern mit pathologischen Befunden möglich. Ein derart konstruiertes Neuronales Netz könnte als unterstützendes Instrument in der Pathologie angewandt werden. Röntgen-Phasenkontrast-CT kann ein leistungsstarkes Werkzeug für eine umfassende, hochpräzise und quantitative 3D-Darstellung und Charakterisierung von gesundem Knorpel und athrotischem Knorpel im Frühstadium bieten, um das Verständnis der Entwicklung von Osteoarthritis zu erweitern

In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging

La osteoporosis es una enfermedad ósea que se manifiesta con una menor densidad ósea y el deterioro de la arquitectura del hueso esponjoso. Ambos factores aumentan la fragilidad ósea y el riesgo de sufrir fracturas óseas, especialmente en mujeres, donde existe una alta prevalencia. El diagnóstico actual de la osteoporosis se basa en la cuantificación de la densidad mineral ósea (DMO) mediante la técnica de absorciometría dual de rayos X (DXA). Sin embargo, la DMO no puede considerarse de manera aislada para la evaluación del riesgo de fractura o los efectos terapéuticos. Existen otros factores, tales como la disposición microestructural de las trabéculas y sus características que es necesario tener en cuenta para determinar la calidad del hueso y evaluar de manera más directa el riesgo de fractura. Los avances técnicos de las modalidades de imagen médica, como la tomografía computarizada multidetector (MDCT), la tomografía computarizada periférica cuantitativa (HR-pQCT) y la resonancia magnética (RM) han permitido la adquisición in vivo con resoluciones espaciales elevadas. La estructura del hueso trabecular puede observarse con un buen detalle empleando estas técnicas. En particular, el uso de los equipos de RM de 3 Teslas (T) ha permitido la adquisición con resoluciones espaciales muy altas. Además, el buen contraste entre hueso y médula que proporcionan las imágenes de RM, así como la utilización de radiaciones no ionizantes sitúan a la RM como una técnica muy adecuada para la caracterización in vivo de hueso trabecular en la enfermedad de la osteoporosis. En la presente tesis se proponen nuevos desarrollos metodológicos para la caracterización morfométrica y mecánica del hueso trabecular en tres dimensiones (3D) y se aplican a adquisiciones de RM de 3T con alta resolución espacial. El análisis morfométrico está compuesto por diferentes algoritmos diseñados para cuantificar la morfología, la complejidad, la topología y los parámetros de anisotropía del tejido trabecular. En cuanto a la caracterización mecánica, se desarrollaron nuevos métodos que permiten la simulación automatizada de la estructura del hueso trabecular en condiciones de compresión y el cálculo del módulo de elasticidad. La metodología desarrollada se ha aplicado a una población de sujetos sanos con el fin de obtener los valores de normalidad del hueso esponjoso. Los algoritmos se han aplicado también a una población de pacientes con osteoporosis con el fin de cuantificar las variaciones de los parámetros en la enfermedad y evaluar las diferencias con los resultados obtenidos en un grupo de sujetos sanos con edad similar.Los desarrollos metodológicos propuestos y las aplicaciones clínicas proporcionan resultados satisfactorios, presentando los parámetros una alta sensibilidad a variaciones de la estructura trabecular principalmente influenciadas por el sexo y el estado de enfermedad. Por otra parte, los métodos presentan elevada reproducibilidad y precisión en la cuantificación de los valores morfométricos y mecánicos. Estos resultados refuerzan el uso de los parámetros presentados como posibles biomarcadores de imagen en la enfermedad de la osteoporosis.Alberich Bayarri, Á. (2010). In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8981Palanci

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Machine Learning in Orthopedics: A Literature Review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance