72 research outputs found
Segmentierung medizinischer Bilddaten und bildgestützte intraoperative Navigation
Die Entwicklung von Algorithmen zur automatischen oder semi-automatischen Verarbeitung von medizinischen Bilddaten hat in den letzten Jahren mehr und mehr an Bedeutung gewonnen. Das liegt zum einen an den immer besser werdenden medizinischen Aufnahmemodalitäten, die den menschlichen Körper immer feiner virtuell abbilden können. Zum anderen liegt dies an der verbesserten Computerhardware, die eine algorithmische Verarbeitung der teilweise im Gigabyte-Bereich liegenden Datenmengen in einer vernünftigen Zeit erlaubt. Das Ziel dieser Habilitationsschrift ist die Entwicklung und Evaluation von Algorithmen für die medizinische Bildverarbeitung. Insgesamt besteht die Habilitationsschrift aus einer Reihe von Publikationen, die in drei übergreifende Themenbereiche gegliedert sind:
-Segmentierung medizinischer Bilddaten anhand von vorlagenbasierten Algorithmen
-Experimentelle Evaluation quelloffener Segmentierungsmethoden unter medizinischen Einsatzbedingungen
-Navigation zur Unterstützung intraoperativer Therapien
Im Bereich Segmentierung medizinischer Bilddaten anhand von vorlagenbasierten Algorithmen wurden verschiedene graphbasierte Algorithmen in 2D und 3D entwickelt, die einen gerichteten Graphen mittels einer Vorlage aufbauen. Dazu gehört die Bildung eines Algorithmus zur Segmentierung von Wirbeln in 2D und 3D. In 2D wird eine rechteckige und in 3D eine würfelförmige Vorlage genutzt, um den Graphen aufzubauen und das Segmentierungsergebnis zu berechnen. Außerdem wird eine graphbasierte Segmentierung von Prostatadrüsen durch eine Kugelvorlage zur automatischen Bestimmung der Grenzen zwischen Prostatadrüsen und umliegenden Organen vorgestellt. Auf den vorlagenbasierten Algorithmen aufbauend, wurde ein interaktiver Segmentierungsalgorithmus, der einem Benutzer in Echtzeit das Segmentierungsergebnis anzeigt, konzipiert und implementiert. Der Algorithmus nutzt zur Segmentierung die verschiedenen Vorlagen, benötigt allerdings nur einen Saatpunkt des Benutzers. In einem weiteren Ansatz kann der Benutzer die Segmentierung interaktiv durch zusätzliche Saatpunkte verfeinern. Dadurch wird es möglich, eine semi-automatische Segmentierung auch in schwierigen Fällen zu einem zufriedenstellenden Ergebnis zu führen.
Im Bereich Evaluation quelloffener Segmentierungsmethoden unter medizinischen Einsatzbedingungen wurden verschiedene frei verfügbare Segmentierungsalgorithmen anhand von Patientendaten aus der klinischen Routine getestet. Dazu gehörte die Evaluierung der semi-automatischen Segmentierung von Hirntumoren, zum Beispiel Hypophysenadenomen und Glioblastomen, mit der frei verfügbaren Open Source-Plattform 3D Slicer. Dadurch konnte gezeigt werden, wie eine rein manuelle Schicht-für-Schicht-Vermessung des Tumorvolumens in der Praxis unterstützt und beschleunigt werden kann. Weiterhin wurde die Segmentierung von Sprachbahnen in medizinischen Aufnahmen von Hirntumorpatienten auf verschiedenen Plattformen evaluiert.
Im Bereich Navigation zur Unterstützung intraoperativer Therapien wurden Softwaremodule zum Begleiten von intra-operativen Eingriffen in verschiedenen Phasen einer Behandlung (Therapieplanung, Durchführung, Kontrolle) entwickelt. Dazu gehört die erstmalige Integration des OpenIGTLink-Netzwerkprotokolls in die medizinische Prototyping-Plattform MeVisLab, die anhand eines NDI-Navigationssystems evaluiert wurde. Außerdem wurde hier ebenfalls zum ersten Mal die Konzeption und Implementierung eines medizinischen Software-Prototypen zur Unterstützung der intraoperativen gynäkologischen Brachytherapie vorgestellt. Der Software-Prototyp enthielt auch ein Modul zur erweiterten Visualisierung bei der MR-gestützten interstitiellen gynäkologischen Brachytherapie, welches unter anderem die Registrierung eines gynäkologischen Brachytherapie-Instruments in einen intraoperativen Datensatz einer Patientin ermöglichte. Die einzelnen Module führten zur Vorstellung eines umfassenden bildgestützten Systems für die gynäkologische Brachytherapie in einem multimodalen Operationssaal. Dieses System deckt die prä-, intra- und postoperative Behandlungsphase bei einer interstitiellen gynäkologischen Brachytherapie ab
Flow pattern analysis for magnetic resonance velocity imaging
Blood flow in the heart is highly complex. Although blood flow patterns have been investigated by both computational modelling and invasive/non-invasive imaging techniques, their evolution and intrinsic connection with cardiovascular disease has yet to be explored. Magnetic resonance (MR) velocity imaging provides a comprehensive distribution of multi-directional in vivo flow distribution so that detailed quantitative analysis of flow patterns is now possible. However, direct visualisation or quantification of vector fields is of little clinical use, especially for inter-subject or serial comparison of changes in flow patterns due to the progression of the disease or in response to therapeutic measures. In order to achieve a comprehensive and integrated description of flow in health and disease, it is necessary to characterise and model both normal and abnormal flows and their effects. To accommodate the diversity of flow patterns in relation to morphological and functional changes, we have described in this thesis an approach of detecting salient topological features prior to analytical assessment of dynamical indices of the flow patterns. To improve the accuracy of quantitative analysis of the evolution of topological flow features, it is essential to restore the original flow fields so that critical points associated with salient flow features can be more reliably detected. We propose a novel framework for the restoration, abstraction, extraction and tracking of flow features such that their dynamic indices can be accurately tracked and quantified. The restoration method is formulated as a constrained optimisation problem to remove the effects of noise and to improve the consistency of the MR velocity data. A computational scheme is derived from the First Order Lagrangian Method for solving the optimisation problem. After restoration, flow abstraction is applied to partition the entire flow field into clusters, each of which is represented by a local linear expansion of its velocity components. This process not only greatly reduces the amount of data required to encode the velocity distribution but also permits an analytical representation of the flow field from which critical points associated with salient flow features can be accurately extracted. After the critical points are extracted, phase portrait theory can be applied to separate them into attracting/repelling focuses, attracting/repelling nodes, planar vortex, or saddle. In this thesis, we have focused on vortical flow features formed in diastole. To track the movement of the vortices within a cardiac cycle, a tracking algorithm based on relaxation labelling is employed. The constraints and parameters used in the tracking algorithm are designed using the characteristics of the vortices. The proposed framework is validated with both simulated and in vivo data acquired from patients with sequential MR examination following myocardial infarction. The main contribution of the thesis is in the new vector field restoration and flow feature abstraction method proposed. They allow the accurate tracking and quantification of dynamic indices associated with salient features so that inter- and intra-subject comparisons can be more easily made. This provides further insight into the evolution of blood flow patterns and permits the establishment of links between blood flow patterns and localised genesis and progression of cardiovascular disease.Open acces
Optimization of neural networks for deep learning and applications to CT image segmentation
[eng] During the last few years, AI development in deep learning has been going so fast that even important researchers, politicians, and entrepreneurs are signing petitions to try to slow it down. The newest methods for natural language processing and image generation are achieving results so unbelievable that people are seriously starting to think they can be dangerous for society. In reality, they are not dangerous (at the moment) even if we have to admit we reached a point where we have no more control over the flux of data inside the deep networks. It is impossible to open a modern
deep neural network and interpret how it processes the information and, in many cases, explain how or why it gives back that particular result. One of the goals of this doctoral work has been to study the behavior of weights in convolutional neural networks and in transformers. We hereby present a work that demonstrates how to invert 3x3 convolutions after training a neural network able to learn how to classify images, with the future aim of having precisely invertible convolutional neural networks. We demonstrate that a simple network can learn to classify images on an open-source dataset without loss in accuracy, with respect to a non-invertible one. All that with the ability to reconstruct the original image without detectable error
(on 8-bit images) in up to 20 convolutions stacked in a row. We present a thorough comparison between our method and the standard. We tested the
performances of the five most used transformers for image classification on an open- source dataset. Studying the embedded matrices, we have been
able to provide two criteria that can help transformers learn with a training time reduction of up to 30% and with no impact on classification accuracy.
The evolution of deep learning techniques is also touching the field of digital health. With tens of thousands of new start-ups and more than 1B $ of investments only in the last year, this field is growing rapidly and promising to revolutionize healthcare. In this thesis, we present several neural networks for the segmentation of lungs, lung nodules, and areas affected by pneumonia induced by COVID-19, in chest CT scans. The architecturesm we used are all residual convolutional neural networks inspired by UNet and Inception. We customized them with novel loss functions and layers
studied to achieve high performances on these particular applications. The errors on the surface of nodule segmentation masks are not over 1mm in more than 99% of the cases. Our algorithm for COVID-19 lesion detection has a specificity of 100% and overall accuracy of 97.1%. In general, it surpasses the state-of-the-art in all the considered statistics, using UNet as a benchmark. Combining these with other algorithms able to detect and predict lung cancer, the whole work was presented in a European innovation program and judged of high interest by worldwide experts.
With this work, we set the basis for the future development of better AI tools in healthcare and scientific investigation into the fundamentals of deep learning.[spa] Durante los últimos años, el desarrollo de la IA en el aprendizaje profundo ha ido tan rápido que Incluso importantes investigadores, políticos y empresarios están firmando peticiones para intentar para ralentizarlo. Los métodos más nuevos para el procesamiento y la generación de imágenes y lenguaje natural, están logrando resultados tan increíbles que la gente está empezando a preocuparse seriamente. Pienso que pueden ser peligrosos para la sociedad. En realidad, no son peligrosos (al menos de momento) incluso si tenemos que admitir que llegamos a un punto en el que ya no tenemos control sobre el flujo de datos dentro de las redes profundas. Es imposible abrir una moderna red neuronal profunda e interpretar cómo procesa la información y, en muchos casos, explique cómo o por qué devuelve ese resultado en particular, uno de los objetivos de este doctorado.
El trabajo ha consistido en estudiar el comportamiento de los pesos en redes neuronales convolucionales y en transformadores. Por la presente presentamos un trabajo que demuestra cómo invertir 3x3 convoluciones después de entrenar una red neuronal capaz de aprender a clasificar imágenes, con el objetivo futuro de tener redes neuronales convolucionales precisamente invertibles. Nosotros queremos demostrar que una red simple puede aprender a clasificar imágenes en un código abierto conjunto de datos sin pérdida de precisión, con respecto a uno no invertible. Todo eso con la capacidad de reconstruir la imagen original sin errores detectables (en imágenes de 8 bits) en hasta 20 convoluciones apiladas en fila. Presentamos una exhaustiva comparación entre nuestro método y el estándar. Probamos las prestaciones de los cinco transformadores más utilizados para la clasificación de imágenes en abierto. conjunto de datos de origen. Al estudiar las matrices incrustadas, hemos sido capaz de proporcionar dos criterios que pueden ayudar a los transformadores a aprender con un tiempo de capacitación reducción de hasta el 30% y sin impacto en la precisión de la clasificación.
La evolución de las técnicas de aprendizaje profundo también está afectando al campo de la salud digital. Con decenas de miles de nuevas empresas y más de mil millones de dólares en inversiones sólo en el año pasado, este campo está creciendo rápidamente y promete revolucionar la atención médica. En esta tesis, presentamos varias redes neuronales para la segmentación de pulmones, nódulos pulmonares, y zonas afectadas por neumonía inducida por COVID-19, en tomografías computarizadas de tórax. La arquitectura que utilizamos son todas redes neuronales convolucionales residuales inspiradas en UNet. Las personalizamos con nuevas funciones y capas de pérdida, estudiado para lograr altos rendimientos en estas aplicaciones particulares. Los errores en la superficie de las máscaras de segmentación de los nódulos no supera 1 mm en más del 99% de los casos. Nuestro algoritmo para la detección de lesiones de COVID-19 tiene una especificidad del 100% y en general precisión del 97,1%. En general supera el estado del arte en todos los aspectos considerados, estadísticas, utilizando UNet como punto de referencia. Combinando estos con otros algoritmos capaces de detectar y predecir el cáncer de pulmón, todo el trabajo se presentó en una innovación europea programa y considerado de gran interés por expertos de todo el mundo.
Con este trabajo, sentamos las bases para el futuro desarrollo de mejores herramientas de IA en Investigación sanitaria y científica sobre los fundamentos del aprendizaje profundo
Advanced Computational Methods for Oncological Image Analysis
[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.
Deep Learning in Medical Image Analysis
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis
Application of Advanced MRI to Fetal Medicine and Surgery
Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution.
This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development.
I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death
Learning to Complete 3D Scenes from Single Depth Images
Building a complete 3D model of a scene given only a single depth image is underconstrained. To acquire a full volumetric model, one typically needs either multiple views, or a single view together with a library of unambiguous 3D models that will fit the shape of each individual object in the scene. In this thesis, we present alternative methods for inferring the hidden geometry of table-top scenes. We first introduce two depth-image datasets consisting of multiple scenes, each with a ground truth voxel occupancy grid. We then introduce three methods for predicting voxel occupancy. The first predicts the occupancy of each voxel using a novel feature vector which measures the relationship between the query voxel and surfaces in the scene observed by the depth camera. We use a Random Forest to map each voxel of unknown state to a prediction of occupancy. We observed that predicting the occupancy of each voxel independently can lead to noisy solutions. We hypothesize that objects of dissimilar semantic classes often share similar 3D shape components, enabling a limited dataset to model the shape of a wide range of objects, and hence estimate their hidden geometry. Demonstrating this hypothesis, we propose an algorithm that can make structured completions of unobserved geometry. Finally, we propose an alternative framework for understanding the 3D geometry of scenes using the observation that individual objects can appear in multiple different scenes, but in different configurations. We introduce a supervised method to find regions corresponding to the same object across different scenes. We demonstrate that it is possible to then use these groupings of partially observed objects to reconstruct missing geometry. We then perform a critical review of the approaches we have taken, including an assessment of our metrics and datasets, before proposing extensions and future work
A Study on Human Motion Acquisition and Recognition Employing Structured Motion Database
九州工業大学博士学位論文 学位記番号:工博甲第332号 学位授与年月日:平成24年3月23日1 Introduction||2 Human Motion Representation||3 Human Motion Recognition||4 Automatic Human Motion Acquisition||5 Human Motion Recognition Employing Structured Motion Database||6 Analysis on the Constraints in Human Motion Recognition||7 Multiple Persons’ Action Recognition||8 Discussion and ConclusionsHuman motion analysis is an emerging research field for the video-based applications capable of acquiring and recognizing human motions or actions. The automaticity of such a system with these capabilities has vital importance in real-life scenarios. With the increasing number of applications, the demand for a human motion acquisition system is gaining importance day-by-day. We develop such kind of acquisition system based on body-parts modeling strategy. The system is able to acquire the motion by positioning body joints and interpreting those joints by the inter-parts inclination. Besides the development of the acquisition system, there is increasing need for a reliable human motion recognition system in recent years. There are a number of researches on motion recognition is performed in last two decades. At the same time, an enormous amount of bulk motion datasets are becoming available. Therefore, it becomes an indispensable task to develop a motion database that can deal with large variability of motions efficiently. We have developed such a system based on the structured motion database concept. In order to gain a perspective on this issue, we have analyzed various aspects of the motion database with a view to establishing a standard recognition scheme. The conventional structured database is subjected to improvement by considering three aspects: directional organization, nearest neighbor searching problem resolution, and prior direction estimation. In order to investigate and analyze comprehensively the effect of those aspects on motion recognition, we have adopted two forms of motion representation, eigenspace-based motion compression, and B-Tree structured database. Moreover, we have also analyzed the two important constraints in motion recognition: missing information and clutter outdoor motions. Two separate systems based on these constraints are also developed that shows the suitable adoption of the constraints. However, several people occupy a scene in practical cases. We have proposed a detection-tracking-recognition integrated action recognition system to deal with multiple people case. The system shows decent performance in outdoor scenarios. The experimental results empirically illustrate the suitability and compatibility of various factors of the motion recognition
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