Towards a Smart Selection of Hybrid Platforms for Multimedia Processing

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.Nowadays, images and videos have been present everywhere, they can come directly from camera, mobile devices or from other peoples that share their images and videos. The latter are used to illustrate different objects in a large number of situations. This makes from image and video processing algorithms a very important tool used for various domains related to computer vision such as video surveillance, medical imaging and database (images and videos) indexation methods. The performance of these algorithms have been so reduced due the the high intensive computation required when using new image and video standards. In this paper, we propose a new framework that allows users to select in a smart and efficient way the processing units (GPU or/and CPU) within heterogeneous systems, when treating different kinds of multimedia objects : single image, multiple images, multiple videos and video in real time. The framework disposes of different image and video primitive functions that are implemented on GPU, such as shape (silhouette) detection, motion tracking using optical flow estimation, edges and corners detection. We have exploited these functions for several situations such as indexing videos, segmenting vertebrae in in X-ray and MR images, detecting and localizing event in multi-user scenarios. Experimentation showed interesting accelerations ranging from 6 to 118, by comparison with sequential implementations. Moreover, the parallel and heterogeneous implementations offered lower power consumption as a result for the fast treatment.European Cooperation in Science and Technology. COS

MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement

Scoliosis diagnosis and assessment depend largely on the measurement of the Cobb angle in spine X-ray images. With the emergence of deep learning techniques that employ landmark detection, tilt prediction, and spine segmentation, automated Cobb angle measurement has become increasingly popular. However, these methods encounter difficulties such as high noise sensitivity, intricate computational procedures, and exclusive reliance on a single type of morphological information. In this paper, we introduce the Multiple Morphology-Aware Network (MMA-Net), a novel framework that improves Cobb angle measurement accuracy by integrating multiple spine morphology as attention information. In the MMA-Net, we first feed spine X-ray images into the segmentation network to produce multiple morphological information (spine region, centerline, and boundary) and then concatenate the original X-ray image with the resulting segmentation maps as input for the regression module to perform precise Cobb angle measurement. Furthermore, we devise joint loss functions for our segmentation and regression network training, respectively. We evaluate our method on the AASCE challenge dataset and achieve superior performance with the SMAPE of 7.28% and the MAE of 3.18{\deg}, indicating a strong competitiveness compared to other outstanding methods. Consequently, we can offer clinicians automated, efficient, and reliable Cobb angle measurement

Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction

Various deep learning models have been proposed for 3D bone shape reconstruction from two orthogonal (biplanar) X-ray images. However, it is unclear how these models compare against each other since they are evaluated on different anatomy, cohort and (often privately held) datasets. Moreover, the impact of the commonly optimized image-based segmentation metrics such as dice score on the estimation of clinical parameters relevant in 2D-3D bone shape reconstruction is not well known. To move closer toward clinical translation, we propose a benchmarking framework that evaluates tasks relevant to real-world clinical scenarios, including reconstruction of fractured bones, bones with implants, robustness to population shift, and error in estimating clinical parameters. Our open-source platform provides reference implementations of 8 models (many of whose implementations were not publicly available), APIs to easily collect and preprocess 6 public datasets, and the implementation of automatic clinical parameter and landmark extraction methods. We present an extensive evaluation of 8 2D-3D models on equal footing using 6 public datasets comprising images for four different anatomies. Our results show that attention-based methods that capture global spatial relationships tend to perform better across all anatomies and datasets; performance on clinically relevant subgroups may be overestimated without disaggregated reporting; ribs are substantially more difficult to reconstruct compared to femur, hip and spine; and the dice score improvement does not always bring a corresponding improvement in the automatic estimation of clinically relevant parameters.Comment: accepted to NeurIPS 202

Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages

In this paper we perform an empirical evaluation of variants of deep learning methods to automatically localize anatomical landmarks in bioimages of fishes acquired using different imaging modalities (microscopy and radiography). We compare two methodologies namely heatmap based regression and multivariate direct regression, and evaluate them in combination with several Convolutional Neural Network (CNN) architectures. Heatmap based regression approaches employ Gaussian or Exponential heatmap generation functions combined with CNNs to output the heatmaps corresponding to landmark locations whereas direct regression approaches output directly the (x, y) coordinates corresponding to landmark locations. In our experiments, we use two microscopy datasets of Zebrafish and Medaka fish and one radiography dataset of gilthead Seabream. On our three datasets, the heatmap approach with Exponential function and U-Net architecture performs better. Datasets and open-source code for training and prediction are made available to ease future landmark detection research and bioimaging applications

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Mechanical and morphometric characterization of cancellous bone

[EN] Bone fracture is a social health problem of increasing magnitude because of its prevalence in aged population due to osteoporosis. Bone quality is often characterized by bone mineral density (BMD) measured at cancellous bone regions using dual-energy X-ray absorptiometry (DXA). However, BMD alone cannot predict several cases because not only density is important, but also microstructure plays an important role in cancellous bone strength. The mechanical properties can be used as indicators of bone integrity as a function of age, disease or treatment. Therefore, cancellous bone fracture characterization and its relationship to microstructure has not been completely solved in the literature and is relevant to improve fracture prediction. In this thesis, we aim at characterizing cancellous bone morphometry and mechanical behavior. Morphometry is estimated through the analysis of micro-computed tomography (micro-CT) images of vertebral cancellous bone specimens. With regards to the mechanical behavior, we calculate elastic, yield and failure properties at the apparent and tissue levels. To determine them, we followed different approaches: compression tests, finite element models and micro-CT phantoms. We have developed finite element models that reproduce the elastic and failure response of cancellous bone under compression conditions. We modeled failure as a combination of continuum damage mechanics and the element deletion technique. The numerical models permitted to estimate elastic and failure properties. Failure properties were consistent with results reported in the literature. Specifically, our results revealed that yield strain is relatively constant (0.7 %) over a range of apparent densities, while failure strain presents a wider range of variation. A single strain parameter (equivalent strain) was found as an accurate descriptor of cancellous bone compression failure. Image-based numerical models usually need for the action of a technician to segment the images. Therefore, we studied the sensitivity to variations of the segmentation threshold on the morphometry and the elastic properties of vertebral cancellous bone specimens of different bone volume fractions. The apparent modulus is highly sensitive to the segmentation threshold. We report variations between 45 and 120 % for a ± 15 % threshold variation. Other parameters, such as BS/BV, BS/TV, Tb.Sp, Tb.N, Conn.D and fractal dimension were influenced significantly. Digital image correlation (DIC) was applied to images taken during compression testing to analyze displacement fields at failure and characterize them. Some variables were explored to describe failure and a study is done about how DIC parameters influence the strain field obtained. Facet and step sizes have a relevant effect on the failure strain estimation, and an increment of both parameters reduces the strain estimation up to 40 %. Besides, several parameters combination led to correct failure pattern detection, so values reported in the literature should be referred to the parameters used. Furthermore, we explored if cancellous bone microstructure acts (non-speckle/texture approach) as a proper pattern to calculate displacements using DIC technique. As regards relationships between microstructure and mechanics, single and multiple parameter analysis were performed to assess the morphometric variables that control the explanation of mechanical properties variation. Bone volume fraction (BV/TV), bone surface to volume ratio (BS/BV), mean trabecular thickness (Tb.Th) and fractal dimension (D) presented the best linear correlations to the elastic properties, while both the yield and failure strains did not show correlation to any morphometric parameter. The regressions obtained permit to estimate those mechanical properties that describe the state of a specimen.[ES] Las fracturas óseas constituyen un problema social de salud con magnitud creciente por su prevalencia en la población de edad avanzada debido a la osteoporosis. La calidad del hueso suele caracterizarse mediante la estimación de la densidad mineral ósea (DMO) en regiones de hueso trabecular, utilizando absorciometría de rayos X de energía dual (DXA). No obstante, la DMO por si sola no es capaz de predecir numerosos casos de fractura porque no solo importa la pérdida de densidad, sino que la microestructura también tiene un papel principal en la resistencia del hueso. Las propiedades mecánicas del hueso pueden usarse como indicadores de su integridad en función de la edad, enfermedad o tratamiento. Por lo tanto, la caracterización de la fractura de hueso trabecular y su relación con la microestructura no se ha resuelto de forma completa en la literatura y es relevante para mejorar las predicciones de fractura. En esta tesis, nuestro principal objetivo es caracterizar la morfometría y el comportamiento mecánico del hueso trabecular. Estimamos la morfometría a través del análisis de imágenes obtenidas por micro tomografía computerizada (micro-CT) de muestras de hueso trabecular vertebral de cerdo. Respecto al comportamiento mecánico, calculamos propiedades elásticas, de plasticidad y fractura a escala aparente y de tejido. Para determinar esas propiedades, hemos seguido diferentes procedimientos: ensayos a compresión, modelos de elementos finitos y fantomas de calibración micro-CT. Los modelos de elementos finitos desarrollados reproducen la respuesta elástica y de fallo bajo condiciones de compresión en hueso trabecular, modelando el fallo como combinación de mecánica del daño contínuo y la técnica de eliminación de elementos. Los modelos numéricos desarrollados han permitido estimar propiedades elásticas y de fallo. En concreto, las deformaciones de inicio de fallo estimadas son relativamente constantes para las muestras analizadas (0.7 %), mientras que las deformaciones últimas de fallo presentan un rango de variación mayor. Por otro lado, encontramos que la deformación equivalente es el descriptor más preciso del fallo a compresión del hueso trabecular. Normalmente, los modelos numéricos basados en imágenes suelen necesitar la acción de un técnico para segmentar las imágenes. En este sentido, estudiamos la sensibilidad de la morfometría y la estimación de propiedades elásticas ante variaciones en el umbral de segmentación en muestras con distinta fracción en volumen. Hemos obtenido que la rigidez aparente es muy sensible a cambios en el umbral de segmentación, con variaciones entre 45 y 120 % para una variación de ± 15 % del umbral de segmentación. Otros parámetros, como BS/BV, BS/TV, Tb.Sp, Tb.N, Conn.D y la dimensión fractal se ven afectados significativamente. Por otro lado, hemos aplicado la técnica correlación digital por imagen (DIC) para caracterizar campos de desplazamientos en el fallo a compresión del hueso trabecular, a partir del análisis de imágenes tomadas durante el ensayo de las muestras. Además, estudiamos la influencia de algunos parámetros de la técnica DIC en el campo de deformaciones obtenido. También, hemos explorado la aplicación DIC sin el uso de moteado, utilizando como patrón de reconocimiento la propia microestructura trabecular. En relación al estudio de la influencia de la microestructura en la respuesta mecánica, hemos calculado correlaciones de uno y varios parámetros para analizar qué variables morfométricas explican la variación de las propiedades mecánicas. La fracción en volumen de hueso (BV/TV), la relación entre el área y el volumen de hueso (BS/BV), el espesor trabecular medio (Tb.Th) y la dimensión fractal (D) presentan las mejores correlaciones lineales respecto a las propiedades elásticas, mientras que las deformaciones de inicio de plasticidad y fractura no mostraron correlación con ningún parámetro morfométrico.[CA] Les fractures òssies constitueixen un problema social de salut amb magnitud creixent per la seua prevalença en la població d'edat avançada a causa de l'osteoporosi. La qualitat de l'os sol caracteritzar-se mitjançant l'estimació de la densitat mineral òssia (DMO) en regions d'os trabecular, utilitzant absorciometria de raigs X d'energia dual (DXA). No obstant això, la DMO per si sola no és capaç de predir nombrosos casos de fractura perquè no sols importa la pèrdua de densitat, sinó que la microestructura també té un paper principal en la resistència de l'os. Les propietats mecàniques de l'os poden usar-se com a indicadors de la seua integritat en funció de l'edat, malaltia o tractament. Per tant, la caracterització de la fractura d'os trabecular i la seua relació amb la microestructura no s'ha resolt de manera completa en la literatura i és rellevant per a millorar les prediccions de fractura. En aquesta tesi, el nostre principal objectiu és caracteritzar la morfometria i el comportament mecànic de l'os trabecular. Estimem la morfometria a través de l'anàlisi d'imatges obtingudes per micro tomografia automatitzada (micro-CT) de mostres d'os trabecular vertebral de porc. Respecte al comportament mecànic, calculem propietats elàstiques, de plasticitat i fractura a escala aparent i de teixit. Per a determinar aqueixes propietats, hem seguit diferents procediments: assajos a compressió, models d'elements finits i fantomas de calibratge micro-CT. Hem desenvolupat models d'elements finits que reprodueixen la resposta elàstica i de fallada sota condicions de compressió en os trabecular, modelant la fallada com a combinació de mecànica del dany continu i la tècnica d'eliminació d'elements. Els models numèrics desenvolupats han permés estimar propietats elàstiques i de fallada. Les nostres estimacions respecte a propietats de fallada són consistents amb valors reportats en la literatura. En concret, les deformacions d'inici de fallada estimades són relativament constants per a les mostres analitzades (0.7 %), mentre que les deformacions últimes de fallada presenten un rang de variació major. D'altra banda, trobem que la deformació equivalent és el descriptor més precís de la fallada a compressió de l'os trabecular. Els models numèrics basats en imatges solen necessitar l'acció d'un tècnic per a segmentar les imatges. En aquest sentit, estudiem la sensibilitat de la morfometria i l'estimació de propietats elàstiques davant variacions en el llindar de segmentació en mostres amb diferent fracció en volum. Hem obtingut que la rigidesa aparent és molt sensible a canvis en el llindar de segmentació, amb variacions entre 45 i 120 % per a una variació de ± 15 % del llindar de segmentació. Altres paràmetres, com BS/BV, BS/TV, Tb.Sp, Tb.N, Conn.D i la dimensió fractal es veuen afectats significativament. D'altra banda, hem aplicat la tècnica correlació digital per imatge (DIC) per a caracteritzar camps de desplaçaments en la fallada a compressió de l'os trabecular, a partir de l'anàlisi d'imatges preses durant l'assaig de les mostres. A més, estudiem la influència d'alguns paràmetres de la tècnica DIC en el camp de deformacions obtingut. També, hem explorat l'aplicació DIC sense l'ús de clapejat, utilitzant com a patró de reconeixement la pròpia microestructura trabecular. En relació a l'estudi de la influència de la microestructura en la resposta mecànica, hem calculat correlacions d'un i diversos paràmetres per a analitzar quines variables morfomètriques expliquen la variació de les propietats mecàniques. La fracció en volum d'os (BV/TV), la relació entre l'àrea i el volum d'os (BS/BV), la espessor trabecular mitjà (Tb.th) i la dimensió fractal (D) presenten les millors correlacions lineals respecte a les propietats elàstiques, mentre que les deformacions d'inici de plasticitat i fractura no van mostrar correlació amb cap paràmetre morfomètric.Belda González, R. (2020). Mechanical and morphometric characterization of cancellous bone [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149376TESI

Development of ultrasound to measure deformation of functional spinal units in cervical spine

Neck pain is a pervasive problem in the general population, especially in those working in vibrating environments, e.g. military troops and truck drivers. Previous studies showed neck pain was strongly associated with the degeneration of intervertebral disc, which is commonly caused by repetitive loading in the work place. Currently, there is no existing method to measure the in-vivo displacement and loading condition of cervical spine on the site. Therefore, there is little knowledge about the alternation of cervical spine functionality and biomechanics in dynamic environments. In this thesis, a portable ultrasound system was explored as a tool to measure the vertebral motion and functional spinal unit deformation. It is hypothesized that the time sequences of ultrasound imaging signals can be used to characterize the deformation of cervical spine functional spinal units in response to applied displacements and loading. Specifically, a multi-frame tracking algorithm is developed to measure the dynamic movement of vertebrae, which is validated in ex-vivo models. The planar kinematics of the functional spinal units is derived from a dual ultrasound system, which applies two ultrasound systems to image C-spine anteriorly and posteriorly. The kinematics is reconstructed from the results of the multi-frame movement tracking algorithm and a method to co-register ultrasound vertebrae images to MRI scan. Using the dual ultrasound, it is shown that the dynamic deformation of functional spinal unit is affected by the biomechanics properties of intervertebral disc ex-vivo and different applied loading in activities in-vivo. It is concluded that ultrasound is capable of measuring functional spinal units motion, which allows rapid in-vivo evaluation of C-spine in dynamic environments where X-Ray, CT or MRI cannot be used.2020-02-20T00:00:00

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\u2019 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