A novel method is presented for analyzing fMRI data, which relies on probabilistic estimates of microanatomically defined regions in individual fMRI volunteers. Postmortem structural and cytoarchitectonic information from the Julich/Dusseldorf group in Germany is aligned to the high-resolution structural MR images of functional MRI volunteers. This is achieved using nonlinear registration, which is applied only to the region of interest. The registered postmortem datasets are then combined into probability maps for microanatomically defined regions that are tailored to the anatomy of individual fMRI volunteers. These are then used as weighted spatial filters on functional MR data. In this thesis, three regions of the primary auditory cortex (located on Heschl's gyrus) have been targeted, and the analysis method is used to explore how these three areas respond to different kinds of sound. Regions Te1.0 and Te1.2 both demonstrate pitch sensitivity, consistent with published observations of the functional response of homologous regions in nonhuman primates. Area Te1.1 displayed sensitivity to both noise and pitch, consistent with the theory that it is homologous with the microanatomically similar area CM in nonhuman primates. Furthermore, the custom probability maps are much less diffuse and anatomically more precise than previous versions generated using the same postmortem data, and therefore permit a more sensitive and anatomically precise analysis of functional activity. This method could be applied to any other microanatomically defined region that has been characterized in the Julich postmortem dataset. / Thesis (Master, Computing) -- Queen's University, 2008-09-26 19:50:54.582

Development of image-guided interventional systems is growing up rapidly in the recent years. These new systems become an essential part of the modern minimally invasive surgical procedures, especially for the cardiac surgery. Transcatheter aortic valve implantation (TAVI) is a recently developed surgical technique to treat severe aortic valve stenosis in elderly and high-risk patients. The placement of stented aortic valve prosthesis is crucial and typically performed under live 2D fluoroscopy guidance. To assist the placement of the prosthesis during the surgical procedure, a new fluoroscopy-based TAVI assistance system has been developed. The developed assistance system integrates a 3D geometrical aortic mesh model and anatomical valve landmarks with live 2D fluoroscopic images. The 3D aortic mesh model and landmarks are reconstructed from interventional angiographic and fluoroscopic C-arm CT system, and a target area of valve implantation is automatically estimated using these aortic mesh models. Based on template-based tracking approach, the overlay of visualized 3D aortic mesh model, landmarks and target area of implantation onto fluoroscopic images is updated by approximating the aortic root motion from a pigtail catheter motion without contrast agent. A rigid intensity-based registration method is also used to track continuously the aortic root motion in the presence of contrast agent. Moreover, the aortic valve prosthesis is tracked in fluoroscopic images to guide the surgeon to perform the appropriate placement of prosthesis into the estimated target area of implantation. An interactive graphical user interface for the surgeon is developed to initialize the system algorithms, control the visualization view of the guidance results, and correct manually overlay errors if needed. Retrospective experiments were carried out on several patient datasets from the clinical routine of the TAVI in a hybrid operating room. The maximum displacement errors were small for both the dynamic overlay of aortic mesh models and tracking the prosthesis, and within the clinically accepted ranges. High success rates of the developed assistance system were obtained for all tested patient datasets. The results show that the developed surgical assistance system provides a helpful tool for the surgeon by automatically defining the desired placement position of the prosthesis during the surgical procedure of the TAVI. / Die Entwicklung bildgeführter interventioneller Systeme wächst rasant in den letzten Jahren. Diese neuen Systeme werden zunehmend ein wesentlicher Bestandteil der technischen Ausstattung bei modernen minimal-invasiven chirurgischen Eingriffen. Diese Entwicklung gilt besonders für die Herzchirurgie. Transkatheter Aortenklappen-Implantation (TAKI) ist eine neue entwickelte Operationstechnik zur Behandlung der schweren Aortenklappen-Stenose bei alten und Hochrisiko-Patienten. Die Platzierung der Aortenklappenprothese ist entscheidend und wird in der Regel unter live-2D-fluoroskopischen Bildgebung durchgeführt. Zur Unterstützung der Platzierung der Prothese während des chirurgischen Eingriffs wurde in dieser Arbeit ein neues Fluoroskopie-basiertes TAKI Assistenzsystem entwickelt. Das entwickelte Assistenzsystem überlagert eine 3D-Geometrie des Aorten-Netzmodells und anatomischen Landmarken auf live-2D-fluoroskopische Bilder. Das 3D-Aorten-Netzmodell und die Landmarken werden auf Basis der interventionellen Angiographie und Fluoroskopie mittels eines C-Arm-CT-Systems rekonstruiert. Unter Verwendung dieser Aorten-Netzmodelle wird das Zielgebiet der Klappen-Implantation automatisch geschätzt. Mit Hilfe eines auf Template Matching basierenden Tracking-Ansatzes wird die Überlagerung des visualisierten 3D-Aorten-Netzmodells, der berechneten Landmarken und der Zielbereich der Implantation auf fluoroskopischen Bildern korrekt überlagert. Eine kompensation der Aortenwurzelbewegung erfolgt durch Bewegungsverfolgung eines Pigtail-Katheters in Bildsequenzen ohne Kontrastmittel. Eine starrere Intensitätsbasierte Registrierungsmethode wurde verwendet, um kontinuierlich die Aortenwurzelbewegung in Bildsequenzen mit Kontrastmittelgabe zu detektieren. Die Aortenklappenprothese wird in die fluoroskopischen Bilder eingeblendet und dient dem Chirurg als Leitfaden für die richtige Platzierung der realen Prothese. Eine interaktive Benutzerschnittstelle für den Chirurg wurde zur Initialisierung der Systemsalgorithmen, zur Steuerung der Visualisierung und für manuelle Korrektur eventueller Überlagerungsfehler entwickelt. Retrospektive Experimente wurden an mehreren Patienten-Datensätze aus der klinischen Routine der TAKI in einem Hybrid-OP durchgeführt. Hohe Erfolgsraten des entwickelten Assistenzsystems wurden für alle getesteten Patienten-Datensätze erzielt. Die Ergebnisse zeigen, dass das entwickelte chirurgische Assistenzsystem ein hilfreiches Werkzeug für den Chirurg bei der Platzierung Position der Prothese während des chirurgischen Eingriffs der TAKI bietet.

Cervical cancer is one of the main causes of death by disease worldwide. In Peru, it holds the first place in frequency and represents 8% of deaths caused by sickness. To detect the disease in the early stages, one of the most used screening tests is the cervix Papanicolaou test. Currently, digital images are increasingly being used to improve Pap test efficiency. This work develops an algorithm based on adaptive thresholds, which will be used in Pap smear assisted quality control software. The first stage of the method is a pre-processing step, in which noise and background removal is done. Next, a block is segmented for each one of the points selected as not background, and a local threshold per block is calculated to search for cell nuclei. If a nucleus is detected, an artifact rejection follows, where only cell nuclei and inflammatory cells are left for the doctors to interpret. The method was validated with a set of 55 images containing 2317 cells. The algorithm successfully recognized 92.3% of the total nuclei in all images collected. / Revisón por pares

There is a big risk that neuroreceptors located in the knee, responsible for our proprioceptive ability, are damaged after an anterior cruciate ligament (ACL) injury occurs. This may cause miscommunication between the neuroreceptors and motoric function in the brain. Due to the brains plasticity, it has been shown that brain activity patterns, presented as blood oxygen dependent level-signal (BOLD-signal), achieved from functional magnetic resonance imaging (fMRI) differs between healthy and injured individuals when performing certain tasks involving knee movement. As there is little consensus on how a proprioceptive test should be performed, a unique test were participants uses blindfold during a knee bending exercise was created at U Motion Lab, Umeå University. A Matlab based general user interface (GUI) was created for evaluation of the proprioceptive test. This GUI is communicating with the third party toolbox SPM12 and performs necessary preprocessing fMRI-image steps for statistical analysis and statistical parametric mapping of the BOLD-signal for both a healthy control- and ACL-injured group. The fMRIimages preprocessed by the GUI were generated by a 3 T GE scanner and the motion data was collected using an eight-camera 3D-motion analysis system. Time events for three different tasks was investigated. These were passive resting, memorizing and proprioceptive events. For both the control (5 participants)- and ACL (2 participants) group the main area of brain activation during the proprioceptive tests occurred in the frontal lobe. For the control group, brain activation was found in the cerebellum anterior lobe which is a possible origin for unconscious proprioception. For the ACL group activation was found in the inferior parietal lobule which involves visuomotor integration. Activation was also found in the inferior frontal gyrus which according to previous studies, may indicate risk-taking/”out of character” decisions. The results of this study indicates that the proprioceptive test seems to be a promising tool for evaluation of proprioceptive ability. However, more subjects need to be included to validate the result of this study.

This dissertation presents and evaluates a methodology for scheduling medical application workloads in virtualized computing environments. Such environments are being widely adopted by providers of “cloud computing” services. In the context of provisioning resources for medical applications, such environments allow users to deploy applications on distributed computing resources while keeping their data secure. Furthermore, higher level services that further abstract the infrastructure-related issues can be built on top of such infrastructures. For example, a medical imaging service can allow medical professionals to process their data in the cloud, easing them from the burden of having to deploy and manage these resources themselves. In this work, we focus on issues related to scheduling scientific workloads on virtualized environments. We build upon the knowledge base of traditional parallel job scheduling to address the specific case of medical applications while harnessing the benefits afforded by virtualization technology. To this end, we provide the following contributions: An in-depth analysis of the execution characteristics of the target applications when run in virtualized environments. A performance prediction methodology applicable to the target environment. A scheduling algorithm that harnesses application knowledge and virtualization-related benefits to provide strong scheduling performance and quality of service guarantees. In the process of addressing these pertinent issues for our target user base (i.e. medical professionals and researchers), we provide insight that benefits a large community of scientific application users in industry and academia. Our execution time prediction and scheduling methodologies are implemented and evaluated on a real system running popular scientific applications. We find that we are able to predict the execution time of a number of these applications with an average error of 15%. Our scheduling methodology, which is tested with medical image processing workloads, is compared to that of two baseline scheduling solutions and we find that it outperforms them in terms of both the number of jobs processed and resource utilization by 20-30%, without violating any deadlines. We conclude that our solution is a viable approach to supporting the computational needs of medical users, even if the cloud computing paradigm is not widely adopted in its current form.

O aumento da geração e do intercâmbio de imagens médicas digitais tem incentivado profissionais da computação a criarem ferramentas para manipulação, armazenamento e busca por similaridade dessas imagens. As ferramentas de recuperação de imagens por conteúdo, foco desse trabalho, têm a função de auxiliar na tomada de decisão e na prática da medicina baseada em estudo de casos semelhantes. Porém, seus principais obstáculos são conseguir uma rápida recuperação de imagens armazenadas em grandes bases e reduzir o gap semântico, caracterizado pela divergência entre o resultado obtido pelo computador e aquele esperado pelo médico. No presente trabalho, uma análise das funções de distância e dos descritores computacionais de características está sendo realizada com o objetivo de encontrar uma aproximação eficiente entre os métodos de extração de características de baixo nível e os parâmetros de percepção do médico (de alto nível) envolvidos na análise de imagens. O trabalho de integração desses três elementos (Extratores de Características, Função de Distância e Parâmetro Perceptual) resultou na criação de operadores de similaridade, que podem ser utilizados para aproximar o sistema computacional ao usuário final, visto que serão recuperadas imagens de acordo com a percepção de similaridade do médico, usuário final do sistema / The continuous growth of the medical images generation and their use in the day-to-day procedures in hospitals and medical centers has motivated the computer science researchers to develop algorithms, methods and tools to store, search and retrieve images by their content. Therefore, the content-based image retrieval (CBIR) field is also growing at a very fast pace. Algorithms and tools for CBIR, which are at the core of this work, can help on the decision making process when the specialist is composing the images analysis. This is based on the fact that the specialist can retrieve similar cases to the one under evaluation. However, the main reservation about the use of CBIR is to achieve a fast and effective retrieval, in the sense that the specialist gets what is expected for. That is, the problem is to bridge the semantic gap given by the divergence among the result automatically delivered by the system and what the user is expecting. In this work it is proposed the perceptual parameter, which adds to the relationship between the feature extraction algorithms and distance functions aimed at finding the best combination to deliver to the user what he/she expected from the query. Therefore, this research integrated the three main elements of similarity queries: the image features, the distance function and the perceptual parameter, what resulted in searching operators. The experiments performed show that these operators can narrow the distance between the system and the specialist, contributing to bridge the semantic gap

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No description available.

Magnetic resonance imaging (MRI) is a widely used medical imaging technique for examinations of the body. However, artifacts are a common problem, that must be handled for reliable diagnoses and to avoid drawing inaccurate conclusions about the contextual insights. Magnetic resonance (MR) images acquired with a Dixon-sequence enables two channels with separate fat and water content. Fat-water inversions, also called swaps, are one common artifact with this method where voxels from the two channels are swapped, producing incorrect data. This thesis investigates the possibility to use deep learning methods for an automatic detection of swaps in MR volumes. The data used in this thesis are MR volumes from UK Biobank, processed by AMRA Medical. Segmentation masks of complicated swaps are created by operators who manually annotate the swap, but only if the regions affect subsequent measurements. The segmentation masks are therefore not fully reliable, and additional synthesized swaps were created. Two different deep learning approaches were investigated, a reconstruction-based method and a segmentation-based method. The reconstruction-based networks were trained to reconstruct a volume as similar as possible to the input volume without any swaps. When testing the network on a volume with a swap, the location of the swap can be estimated from the reconstructed volume with postprocessing methods. Autoencoders is an example of a reconstruction-based network. The segmentation-based models were trained to segment a swap directly from the input volume, thus using volumes with swaps both during training and testing. The segmentation-based networks were inspired by a U-Net. The performance of the models from both approaches was evaluated on data with real and synthetic swaps with the metrics: Dice coefficient, precision, and recall. The result shows that the reconstruction-based models are not suitable for swap detection. Difficulties in finding the right architecture for the models resulted in bad reconstructions, giving unreliable predictions. Further investigations in different post-processing methods, architectures, and hyperparameters might improve swap detection. The segmentation-based models are robust with reliable detections independent of the size of the swaps, despite being trained on data with synthesized swaps. The results from the models look very promising, and can probably be used as an automated method for swap detection with some further fine-tuning of the parameters.

By combining computed tomography data with computational fluid dynamics, the cardiac hemodynamics of a patient can be assessed for diagnosis and treatment of cardiac disease. The advantage of computed tomography over other medical imaging modalities is its capability of producing detailed high resolution images containing geometric measurements relevant to the simulation of cardiac blood flow. To extract these geometries from computed tomography data, segmentation of 4D cardiac computed tomography (CT) data has been performed using two deep learning frameworks that combine methods which have previously shown success in other research. The aim of this thesis work was to develop and evaluate a deep learning based technique to segment the left ventricle, ascending aorta, left atrium, left atrial appendage and the proximal pulmonary vein inlets. Two frameworks have been studied where both utilise a 2D multi-axis implementation to segment a single CT volume by examining it in three perpendicular planes, while one of them has also employed a 3D binary model to extract and crop the foreground from surrounding background. Both frameworks determine a segmentation prediction by reconstructing three volumes after 2D segmentation in each plane and combining their probabilities in an ensemble for a 3D output.  The results of both frameworks show similarities in their performance and ability to properly segment 3D CT data. While the framework that examines 2D slices of full size volumes produces an overall higher Dice score, it is less successful than the cropping framework at segmenting the smaller left atrial appendage. Since the full size 2D slices also contain background information in each slice, it is believed that this is the main reason for better segmentation performance. While the cropping framework provides a higher proportion of each foreground label, making it easier for the model to identify smaller structures. Both frameworks show success for use in 3D cardiac CT segmentation, and with further research and tuning of each network, even better results can be achieved.