Accurate automated Cobb angles estimation using multi-view extrapolation net.

Accurate automated quantitative Cobb angle estimation that quantitatively evaluates scoliosis plays an important role in scoliosis diagnosis and treatment. It solves the problem of the traditional manual method, which is the current clinical standard for scoliosis assessment, but time-consuming and unreliable. However, it is very challenging to achieve highly accurate automated Cobb angle estimation because it is difficult to utilize the information of Anterior-posterior (AP) and Lateral (LAT) view X-rays efficiently. We therefore propose a Multi-View Extrapolation Net (MVE-Net) that provides accurate automated scoliosis estimation in multi-view (both AP and LAT) X-rays. The MVE-Net consists of three parts: Joint-view net learning AP and LAT angles jointly based on landmarks learned from joint representation; Independent-view net learning AP and LAT angles independently based on landmarks learned from unique independent feature of AP or LAT angles; Inter-error correction net learning a combination function adaptively to offset the first two nets’ errors for accurate angle estimation. Experimental results on 526 X-rays show 7.81 and 6.26 Circular Mean Absolute Error in AP and LAT angle estimation, which shows the MVE-Net provides an accurate Cobb angle estimation in multi-view X-rays. Our method therefore provides effective framework for automated, accurate, and reliable scoliosis estimation

Accurate automated Cobb angles estimation using multi-view extrapolation net

Abstract(#br)Accurate automated quantitative Cobb angle estimation that quantitatively evaluates scoliosis plays an important role in scoliosis diagnosis and treatment. It solves the problem of the traditional manual method, which is the current clinical standard for scoliosis assessment, but time-consuming and unreliable. However, it is very challenging to achieve highly accurate automated Cobb angle estimation because it is difficult to utilize the information of Anterior-posterior (AP) and Lateral (LAT) view X-rays efficiently. We therefore propose a Multi-View Extrapolation Net (MVE-Net) that provides accurate automated scoliosis estimation in multi-view (both AP and LAT) X-rays. The MVE-Net consists of three parts: Joint-view net learning AP and LAT angles jointly based on landmarks learned from joint representation; Independent-view net learning AP and LAT angles independently based on landmarks learned from unique independent feature of AP or LAT angles; Inter-error correction net learning a combination function adaptively to offset the first two nets’ errors for accurate angle estimation. Experimental results on 526 X-rays show 7.81 and 6.26 Circular Mean Absolute Error in AP and LAT angle estimation, which shows the MVE-Net provides an accurate Cobb angle estimation in multi-view X-rays. Our method therefore provides effective framework for automated, accurate, and reliable scoliosis estimation

Automation of Spine Curve Assessment in Frontal Radiographs Using Deep Learning of Vertebral-tilt Vector

In this paper, an automated and visually explainable system is proposed for a scoliosis assessment from spinal radiographs, which deals with the drawback of manual measurements, which are known to be time-consuming, cumbersome, and operator dependent. Deep learning techniques have been successfully applied in the accurate extraction of Cobb angle measurements, which is the gold standard for a scoliosis assessment. Such deep learning methods directly estimate the Cobb angle without providing structural information of the spine which can be used for diagnosis. Although conventional segmentationbased methods can provide the spine structure, they still have limitations in the accurate measurement of the Cobb angle. It would be desirable to build a clinician-friendly diagnostic system for scoliosis that provides not only an automated Cobb angle assessment but also local and global structural information of the spine. This paper addresses this need through the development of a hierarchical method which consisting of three major parts. (1) A confidence map is used to selectively localize and identify all vertebrae in an accurate and robust manner, (2) vertebral-tilt field is used to estimate the slope of an individual vertebra, and (3) the Cobb angle is determined by combining the vertebral centroids with the previously obtained vertebral-tilt field. The performance of the proposed method was validated, resulting in circular mean absolute error of 3:51 and symmetric mean absolute percentage error of 7:84% for the Cobb angle.ope

Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation

Scoliosis is a three-dimensional deformity of the spine, most often diagnosed in childhood. It affects 2-3% of the population, which is approximately seven million people in North America. Currently, the reference standard for assessing scoliosis is based on the manual assignment of Cobb angles at the site of the curvature center. This manual process is time consuming and unreliable as it is affected by inter- and intra-observer variance. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model. The proposed method first segments the vertebrae in an X-Ray image using YOLACT, then it tracks the important landmarks using the minimum bounding box approach. Lastly, the extracted landmarks are used to calculate the corresponding Cobb angles. The model achieved a Symmetric Mean Absolute Percentage Error (SMAPE) score of 10.76%, demonstrating the reliability of this process in both vertebra localization and Cobb angle measurement

Identifying the Severity of Adolescent Idiopathic Scoliosis During Gait by Using Machine Learning

La scoliose idiopathique de l'adolescent (SIA) est une déformation de la colonne vertébrale dans les trois plans de l’espace objectivée par un angle de Cobb ≥ 10°. Celle-ci affecte les adolescents âgés entre 10 et 16 ans. L’étiologie de la scoliose demeure à ce jour inconnue malgré des recherches approfondies. Différentes hypothèses telles que l’implication de facteurs génétiques, hormonaux, biomécaniques, neuromusculaires ou encore des anomalies de croissance ont été avancées. Chez ces adolescents, l'ampleur de la déformation de la colonne vertébrale est objectivée par mesure manuelle de l’angle de Cobb sur radiographies antéropostérieures. Cependant, l’imprécision inter / intra observateur de cette mesure, ainsi que de l’exposition fréquente (biannuelle) aux rayons X que celle-ci nécessite pour un suivi adéquat, sont un domaine qui préoccupe la communauté scientifique et clinique. Les solutions proposées à cet effet concernent pour beaucoup l'utilisation de méthodes assistées par ordinateur, telles que des méthodes d'apprentissage machine utilisant des images radiographiques ou des images du dos du corps humain. Ces images sont utilisées pour classer la sévérité de la déformation vertébrale ou pour identifier l'angle de Cobb. Cependant, aucune de ces méthodes ne s’est avérée suffisamment précise pour se substituer l’utilisation des radiographies. Parallèlement, les recherches ont démontré que la scoliose modifie le schéma de marche des personnes qui en souffrent et par conséquent également les efforts intervertébraux. C’est pourquoi, l'objectif de cette thèse est de développer un modèle non invasif d’identification de la sévérité de la scoliose grâce aux mesures des efforts intervertébraux mesurés durant la marche. Pour atteindre cet objectif, nous avons d'abord comparé les efforts intervertébraux calculés par un modèle dynamique multicorps, en utilisant la dynamique inverse, chez 15 adolescents atteints de SIA avec différents types de courbes et de sévérités et chez 12 adolescents asymptomatiques (à titre comparatif). Par cette comparaison, nous avons pu objectiver que les efforts intervertébraux les plus discriminants pour prédire la déformation vertébrale étaient la force et le couple antéro-postérieur et la force médio-latérale. Par la suite, nous nous sommes concentrés sur la classification de la sévérité de la déformation vertébrale de 30 AIS ayant une courbure thoraco-lombaire / lombaire. Pour ce faire, nous avons testé différents modèles de classification. L'angle de Cobb a été identifié en exécutant différents modèles de régression. Les caractéristiques (features) servant à alimenter les algorithmes d'entraînement ont été choisies en fonction des efforts intervertébraux les plus pertinents à la déformation vertébrale au niveau de la charnière lombo-sacrée (vertèbres allantes de L5-S1). Les précisions les plus élevées pour la classification exécutant différents algorithmes ont été obtenues par un algorithme de classification d'ensemble comprenant les “K-nearest neighbors”, “Support vector machine”, “Random forest”, “multilayer perceptron”, et un modèle de “neural networks” avec une précision de 91.4% et 93.6%, respectivement. De même, le modèle de régression par “Decision tree” parmi les autres modèles a obtenu le meilleur résultat avec une erreur absolue moyenne égale à 4.6° de moyenne de validation croisée de 10 fois. En conclusion, nous pouvons dire que cette étude démontre une relation entre la déformation de la colonne vertébrale et les efforts intervertébraux mesurés lors de la marche. L'angle de Cobb a été identifié à l'aide d'une méthode sans rayonnement avec une précision prometteuse égale à 4.6°. Il s’agit d’une amélioration majeure par rapport aux méthodes précédemment proposées ainsi que par rapport à la mesure classique réalisée par des spécialistes présentant une erreur entre 5° et 10° (ceci en raison de la variation intra/inter observateur). L’algorithme que nous vous présentons peut être utilisé comme un outil d'évaluation pour suivre la progression de la scoliose. Il peut être considéré comme une alternative à la radiographie. Des travaux futurs devraient tester l'algorithme et l’adapter pour d’autres formes de SIA, telles que les scolioses lombaire ou thoracolombaire.----------ABSTRACT Adolescent idiopathic scoliosis (AIS) is a 3D deformation of the spine and rib cage greater than 10° that affects adolescents between the ages of 10 and 16 years old. The true etiology is unknown despite extensive research and investigation. However, different theories such as genetic and hormonal factors, growth abnormalities or biomechanical and neuromuscular reasons have been proposed as possible causes. The magnitude of spinal deformity in AIS is measured by the Cobb angle in degrees as the gold standard through the X-rays by specialists. The inter/intra observer error and the cumulative exposure to radiation, however, are sources of increasing concern among researchers with regards to the accuracy of manual measurement. Proposed solutions have therefore, focused on using computer-assisted methods such as Machine Learning using X-ray images, and/or trunk images to classify the severity of spinal deformity or to identify the Cobb angle. However, none of the proposed methods have shown the level of accuracy required for use as an alternative to X-rays. Meanwhile, scoliosis has been recognized as a pathology that modifies the gait pattern, subsequently impinging upon intervertebral efforts. The present thesis aims to develop a radiation-free model to identify the severity of idiopathic scoliosis in adolescents based on the intervertebral efforts during gait. To accomplish this objective, we compared the intervertebral efforts computed using a multibody dynamics model, by way of inverse dynamics, among 15 adolescents with AIS having different curve types and severities, as well as 12 typically developed adolescents. This resulted in the identification of the most relevant intervertebral efforts influenced by spinal deformity: mediolateral (ML) force; anteroposterior (AP) force; and torque. Additionally, we focused on the classification of the severity of spinal deformity among 30 AIS with thoracolumbar/lumbar curvature, testing different classification models. Lastly, the Cobb angle was identified running regression models. The features to feed training algorithms were chosen based on the most relevant intervertebral efforts to the spinal deformity on the lumbosacral (L5-S1) joint. The highest accuracies for the classification were obtained by the ensemble classifier algorithm, including “K-nearest neighbors”, “support vector machine”, “random forest”, and “multilayer perceptron”, as well as a neural network model with an accuracy of 91.4% and 93.6%, respectively. Likewise, the “decision tree regression” model achieved the best result with a mean absolute error equal to 4.6 degrees of an averaged 10-fold cross-validation. This study shows a relation between spinal deformity and the produced intervertebral efforts during gait. The Cobb angle was identified using a radiation-free method with a promising accuracy, providing a mean absolute error of 4.6°. Compared to measurement variations, ranging between 5° and 10° in the manual Cobb angle measurements by specialists, the proposed model provided reliable accuracy. This algorithm can be used as an assessment tool, alternative to the X-ray radiography, to follow up the progression of scoliosis. As future work, the algorithm should be tested and modified on AIS with other types of spine curvature than lumbar/thoracolumbar

Aeronautical Engineering: A continuing bibliography with indexes (supplement 177)

This bibliography lists 469 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1984

Quantifying groundwater-surface water interactions to improve the outcomes of human activities

Interactions between surface water and groundwater impact both the quality and quantity of water resources. This dissertation is focused on the interactions between surface water in streams and groundwater in hyporheic zones and shallow fluvial aquifers, and how human beings influence these interactions and are influenced by them. The three chapters of this dissertation span scales from cm to km and locations from Upstate New York to the Peruvian Andes, but are united by the goal to improve the scientific understanding of groundwater-surface water interactions (GWSWI) using novel techniques, in order to improve the outcomes of human activities for people and ecosystems. Heat is a useful tracer for quantifying GWSWI, but analyzing large amounts of raw thermal data has many challenges. Chapter 1 presents a computer program named VFLUX for processing raw temperature time series and calculating vertical water flux in shallow sub-surface-water systems. The workflow synthesizes several recent advancements in signal processing, and adds new techniques for calculating flux rates with large numbers of temperature records from high-resolution sensor profiles. The program includes functions for quantitatively evaluating the ideal spacing between sensor pairs, and for performing error and sensitivity analyses for the heat transport model due to thermal parameter uncertainty. The new method is demonstrated by processing two field temperature time series datasets collected using discrete temperature sensors and a high-resolution DTS profile. The analyses of field data show vertical flux rates significantly decreasing with depth at high-spatial resolution as the sensor profiles penetrate shallow, curved hyporheic flow paths, patterns which may have been obscured without the unique analytical abilities of VFLUX. Natural channel design restoration projects in streams often include the construction of cross-vanes, which are stone, dam-like structures that span the active channel, and are often thought to increase local hyporheic exchange. In Chapter 2, vertical hyporheic exchange flux (HEF) and redox-sensitive solutes were measured in the streambed around 4 cross-vanes with different morphologies. Observed patterns of HEF and redox conditions are not dominated by a single, downstream-directed hyporheic flow cell beneath cross-vanes. Instead, spatial patterns of moderate ( Melting tropical glaciers supply approximately half of dry season stream discharge in glacierized valleys of the Cordillera Blanca, Peru. The remainder of streamflow originates as groundwater stored in alpine meadows, moraines and talus slopes. A better understanding of the dynamics of alpine groundwater, including sources and contributions to streamflow and GWSWI, is important for making accurate estimates of glacial inputs to the hydrologic budget, and for our ability to make predictions about future water resources as glaciers retreat. The field study described in Chapter 3 focused on two high-elevation meadows in valleys of the Blanca. Tracer measurements of stream and spring discharge and groundwater-surface water exchange were combined with synoptic sampling of water isotopic and geochemical composition, in order to characterize and quantify contributions to streamflow from different geomorphic features. In a valley headwaters study site, groundwater supplied approximately half of stream discharge from the meadow, with most originating in a debris fan adjacent to the meadow and little from the meadow itself (6%); however, in at a mid-valley study site, where meadows are extensive, local groundwater has a large impact on stream flow and chemistry through large net discharge and fractional hydrologic turnover

Prüfkopfpositionsverfolgung durch Auswertung von akustischen Bildfolgen für eine quantitativ bewertbare manuelle Ultraschallprüfung

Ultrasonic inspection methods are used to prevent failure of pressurized components. However, flaw evaluation depends on skills of trained inspectors. Automated scanning has become mandatory for more reliable inspection performance. However, manipulator techniques are often not applicable. The control of transducer position during manual scanning could provide an equivalent inspection quality but with the advantage of easy access and scanning. Ultrasonic multi-channel equipment enables real-time imaging of inspection results during scanning. Migration codes reconstruct reflector images with a resolution given by the aperture of the transducer array element. Optical flow algorithm can be applied to identify image changes of sector scan sequences of linear arrays for quantitative transducer motion tracking. These are the basics for the development of an “Acoustic Mouse”, so called as analogous to the optical mouse of computer technology. We developed an optimized optical flow algorithm for linear arrays. By optical flow estimate of sector scan sequences we could demonstrate a transducer positioning accuracy better than half of the wavelength. However, optical flow estimate of noise images requires the use of longitudinal waves and appropriate focusing to reduce the contribution of grain boundary reflection. Further, optical flow images can be used to identify transducer positions after lift-off situations. The time needed for transducer repositioning is in the range of 2 seconds.Die Ultraschall Impuls-Echo Methode zeichnet sich durch eine hohe Empfindlichkeit beim Nachweis von rißartigen Fehlern aus, die zum Versagen von druckführenden Komponenten führen können. Allerdings kann nur mit großem Aufwand eine hinreichend sichere Aussage über Fehlerart und Größe getroffen werden. Dies führt zur Forderung, kritische Bauteile automatisiert zu prüfen. Eine manuelle Prüfung würde eine Erfassung der Prüfkopfposition erfordern. Mit Gruppenstrahlern kann ein Reflektorbild in Echtzeit gemessen werden. Für die Rekonstruktion werden Migrationsalgorithmen verwendet, mit denen eine Auflösung bis zu einer halben Wellenlänge erreicht werden kann. Damit sind die Grundlagen gegeben für die Entwicklung einer akustischen Maus, ein Analogon zur optischen Maus der Computertechnik. Die Tauglichkeit dieses Konzeptes wurde nachgewiesen: ein optimierter Algorithmus zur Bestimmung von Bildänderungen und der anschließenden Prüfkopfpositionsbestimmung wurde entwickelt und experimentell überprüft. Der Algorithmus basiert auf den Prinzipien des optischen Flusses. Die Analyse von Rauschbildern setzt die Verwendung longitudinaler Wellen und eine gute Fokussierung zur Einschränkung des Beitrages der Reflektionen an den Korngrenzen voraus. Die optischen Flußbilder können als Positionssignatur verwendet werden, die nach einer Prüfkopfabhebung das Auffinden der bereits geprüften Positionen ermöglicht. Die erreichten Genauigkeiten liegen im Bereich einer halben Wellenlänge

Prüfkopfpositionsverfolgung durch Auswertung von akustischen Bildfolgen für eine quantitativ bewertbare manuelle Ultraschallprüfung

Ultrasonic inspection methods are used to prevent failure of pressurized components. However, flaw evaluation depends on skills of trained inspectors. Automated scanning has become mandatory for more reliable inspection performance. However, manipulator techniques are often not applicable. The control of transducer position during manual scanning could provide an equivalent inspection quality but with the advantage of easy access and scanning. Ultrasonic multi-channel equipment enables real-time imaging of inspection results during scanning. Migration codes reconstruct reflector images with a resolution given by the aperture of the transducer array element. Optical flow algorithm can be applied to identify image changes of sector scan sequences of linear arrays for quantitative transducer motion tracking. These are the basics for the development of an “Acoustic Mouse”, so called as analogous to the optical mouse of computer technology. We developed an optimized optical flow algorithm for linear arrays. By optical flow estimate of sector scan sequences we could demonstrate a transducer positioning accuracy better than half of the wavelength. However, optical flow estimate of noise images requires the use of longitudinal waves and appropriate focusing to reduce the contribution of grain boundary reflection. Further, optical flow images can be used to identify transducer positions after lift-off situations. The time needed for transducer repositioning is in the range of 2 seconds.Die Ultraschall Impuls-Echo Methode zeichnet sich durch eine hohe Empfindlichkeit beim Nachweis von rißartigen Fehlern aus, die zum Versagen von druckführenden Komponenten führen können. Allerdings kann nur mit großem Aufwand eine hinreichend sichere Aussage über Fehlerart und Größe getroffen werden. Dies führt zur Forderung, kritische Bauteile automatisiert zu prüfen. Eine manuelle Prüfung würde eine Erfassung der Prüfkopfposition erfordern. Mit Gruppenstrahlern kann ein Reflektorbild in Echtzeit gemessen werden. Für die Rekonstruktion werden Migrationsalgorithmen verwendet, mit denen eine Auflösung bis zu einer halben Wellenlänge erreicht werden kann. Damit sind die Grundlagen gegeben für die Entwicklung einer akustischen Maus, ein Analogon zur optischen Maus der Computertechnik. Die Tauglichkeit dieses Konzeptes wurde nachgewiesen: ein optimierter Algorithmus zur Bestimmung von Bildänderungen und der anschließenden Prüfkopfpositionsbestimmung wurde entwickelt und experimentell überprüft. Der Algorithmus basiert auf den Prinzipien des optischen Flusses. Die Analyse von Rauschbildern setzt die Verwendung longitudinaler Wellen und eine gute Fokussierung zur Einschränkung des Beitrages der Reflektionen an den Korngrenzen voraus. Die optischen Flußbilder können als Positionssignatur verwendet werden, die nach einer Prüfkopfabhebung das Auffinden der bereits geprüften Positionen ermöglicht. Die erreichten Genauigkeiten liegen im Bereich einer halben Wellenlänge