Skeletal muscle fascia is a connective tissue which provides structure and aidswith force transfer in a muscle. Currently there are no good ways of detectingand analyzing micrometer thick structures of this tissue in-vivo. In this thesis,we created a model to detect skeletal muscle fascia, and tested its performanceusing simulated data. Utilizing the ultrasound simulation software Vantage,which operates through MATLAB, we created a simulation model which repli-cates the properties and behaviour of skeletal muscle fascia. To detect thetissue, we changed and adapted a previously implemented model of ultrasoundlocalization microscopy (ULM), previously only used to create super resolutionimages of blood vessels. Finally we evaluated the models ability to locate anddetermine the thickness of the simulated fascia. Additionally we tested themodels ability to separate adjacent objects.We found that our model was successful at detecting and localizing thesimulated fascia, with a sub wavelength accuracy. The precision of the locatedfascia appears more accurate for horizontally aligned objects compared to thevertically aligned ones. The results from determining the thickness of the fasciaproved relatively successful as well. However the results showed a high variance.This could be improved through an inclusion of stocasticity in the simulationmodel we developed. Finally the ability to distinguish two objects close to eachother showed successful results as well. The method was able to clearly detecta fascia circle with a 0.5mm diameter. It was unable to detect the sides a fasciacircle with a 0.25mm diameter.The main limitation with the model we have developed lies in the simulationsperformed. The simulation model we used was very basic, meaning that it didnot perfectly represent the skeletal muscle fascia we sought to examine. Furtherdevelopment of the simulation model is required to provide a result which ismore representative of real skeletal muscle fascia.The analysis of this first model shows promise in detecting the simplifiedfascia provided by our simulation model. At this stage, the method will requiremore extensive testing, together with a more thorough statistical analysis, beforewe can state the usefulness of the method.

Machine learning applications within medical imaging often suffer from a lack of data, as a consequence of restrictions that hinder the free distribution of patient information. In this project, GANs (generative adversarial networks) are used to generate data synthetically, in an effort to circumvent this issue. The GAN framework PGAN is trained on the brain tumor segmentation dataset BraTS to generate new, synthetic brain tumor masks with the same visual characteristics as the real samples. The image-to-image translation network SPADE is subsequently trained on the image pairs in the real dataset, to learn a transformation from segmentation masks to brain MR images, and is in turn used to map the artificial segmentation masks generated by PGAN to corresponding artificial MR images. The images generated by these networks form a new, synthetic dataset, which is used to augment the original dataset. Different quantities of real and synthetic data are then evaluated in three different brain tumor segmentation tasks, where the image segmentation network U-Net is trained on this data to segment (real) MR images into the classes in question. The final segmentation performance of each training instance is evaluated over test data from the real dataset with the Weighted Dice Loss metric. The results indicate a slight increase in performance across all segmentation tasks evaluated in this project, when including some quantity of synthetic images. However, the differences were largest when the experiments were restricted to using only 20 % of the real data, and less significant when the full dataset was made available. A majority of the generated segmentation masks appear visually convincing to an extent (although somewhat noisy with regards to the intra-tumoral classes), while a relatively large proportion appear heavily noisy and corrupted. However, the translation of segmentation masks to MR images via SPADE proved more reliable and consistent.

Colorectal cancer is one of the most prevalent malignancies globally and recently introduced digital pathology enables the use of machine learning as an aid for fast diagnostics. This project aimed to develop a deep neural network model to specifically identify and differentiate dysplasia in the epithelium of colorectal polyps and was posed as a binary classification problem. The available dataset consisted of 80 whole slide images of different H&E-stained polyp sections, which were parted info smaller patches, annotated by a pathologist. The best performing model was a pre-trained ResNet-18 utilising a weighted sampler, weight decay and augmentation during fine tuning. Reaching an area under precision-recall curve of 0.9989 and 97.41% accuracy on previously unseen data, the model’s performance was determined to underperform compared to the task’s intra-observer variability and be in alignment with the inter-observer variability. Final model made publicly available at https://github.com/stinabr/classification-of-colorectal-polyps.

Segmentation of the brain into sub-volumes has many clinical applications. Manyneurological diseases are connected with brain atrophy (tissue loss). By dividingthe brain into smaller compartments, volume comparison between the compartmentscan be made, as well as monitoring local volume changes over time. Theformer is especially interesting for the left and right cerebral hemispheres, dueto their symmetric appearance. By using automatic segmentation, the time consumingstep of manually labelling the brain is removed, allowing for larger scaleresearch.In this thesis, three automatic methods for segmenting the brain from magneticresonance (MR) images are implemented and evaluated. Since neither ofthe evaluated methods resulted in sufficiently good segmentations to be clinicallyrelevant, a novel segmentation method, called SB-GC (shape bottleneck detectionincorporated in graph cuts), is also presented. SB-GC utilizes quantitative MRIdata as input data, together with shape bottleneck detection and graph cuts tosegment the brain into the left and right cerebral hemispheres, the cerebellumand the brain stem. SB-GC shows promises of highly accurate and repeatable resultsfor both healthy, adult brains and more challenging cases such as childrenand brains containing pathologies.

Image guidance and visualization play an important role in modern surgery to help surgeons perform their surgical procedures. Here, the focus is on neurosurgery applications, in particular brain tumor surgery where a craniotomy (opening of the skull) is performed to access directly the brain region to be treated. In this type of surgery, once the skull is opened the brain can change its shape, and this deformation is known as brain shift. Moreover, the boundaries of many types of tumors are difficult to identify by the naked eye from healthy tissue. The main goal of this work was to study and develop image guidance and visualization methods for tumor surgery in order to overcome the problems faced in this type of surgery. Due to brain shift the magnetic resonance dataset acquired before the operation (preoperatively) no longer corresponds to the anatomy of the patient during the operation (intraoperatively). For this reason, in this work methods were studied and developed to compensate for this deformation. To guide the deformation methods, information of the superficial vessel centerlines of the brain was used. A method for accurate (approximately 1 mm) reconstruction of the vessel centerlines using a multiview camera system was developed. It uses geometrical constraints, relaxation labeling, thin plate spline filtering and finally mean shift to find the correct correspondences between the camera images. A complete non-rigid deformation pipeline was initially proposed and evaluated with an animal model. From these experiments it was observed that although the traditional non-rigid registration methods (in our case coherent point drift) were able to produce satisfactory vessel correspondences between preoperative and intraoperative vessels, in some specific areas the results were suboptimal. For this reason a new method was proposed that combined the coherent point drift and thin plate spline semilandmarks. This combination resulted in an accurate (below 1 mm) non-rigid registration method, evaluated with simulated data where artificial deformations were performed. Besides the non-rigid registration methods, a new rigid registration method to obtain the rigid transformation between the magnetic resonance dataset and the neuronavigation coordinate systems was also developed. Once the rigid transformation and the vessel correspondences are known, the thin plate spline can be used to perform the brain shift deformation. To do so, we have used two approaches: a direct and an indirect. With the direct approach, an image is created that represents the deformed data, and with the indirect approach, a new volume is first constructed and only after that can the deformed image be created. A comparison of these two approaches, implemented for the graphics processing units, in terms of performance and image quality, was performed. The indirect method was superior in terms of performance if the sampling along the ray is high, in comparison to the voxel grid, while the direct was superior otherwise. The image quality analysis seemed to indicate that the direct method is superior. Furthermore, visualization studies were performed to understand how different rendering methods and parameters influence the perception of the spatial position of enclosed objects (typical situation of a tumor enclosed in the brain). To test these methods a new single-monitor-mirror stereoscopic display was constructed. Using this display, stereo images simulating a tumor inside the brain were presented to the users with two rendering methods (illustrative rendering and simple alpha blending) and different levels of opacity. For the simple alpha blending method an optimal opacity level was found, while for the illustrative rendering method all the opacity levels used seemed to perform similarly. In conclusion, this work developed and evaluated 3D reconstruction, registration (rigid and non-rigid) and deformation methods with the purpose of minimizing the brain shift problem. Stereoscopic perception of the spatial position of enclosed objects was also studied using different rendering methods and parameter values.

Detection and positioning of anatomical landmarks, also called points of interest(POI), is often a concept of interest in medical image processing. Different measures or automatic image analyzes are often directly based upon positions of such points, e.g. in organ segmentation or tissue quantification. Manual positioning of these landmarks is a time consuming and resource demanding process. In this thesis, a general method for positioning of anatomical landmarks is outlined, implemented and evaluated. The evaluation of the method is limited to three different POI; left femur head, right femur head and vertebra T9. These POI are used to define the range of the abdomen in order to measure the amount of abdominal fat in 3D data acquired with quantitative magnetic resonance imaging (MRI). By getting more detailed information about the abdominal body fat composition, medical diagnoses can be issued with higher confidence. Examples of applications could be identifying patients with high risk of developing metabolic or catabolic disease and characterizing the effects of different interventions, i.e. training, bariatric surgery and medications. The proposed method is shown to be highly robust and accurate for positioning of left and right femur head. Due to insufficient performance regarding T9 detection, a modified method is proposed for T9 positioning. The modified method shows promises of accurate and repeatable results but has to be evaluated more extensively in order to draw further conclusions.

Segundo o Instituto Nacional do Câncer (INCA), o câncer de colorretal é o terceiro tipo de câncer mais comum entre os homens e o segundo entre as mulheres. Atualmente a avaliação visual feita por um patologista é o principal método utilizado para o diagnóstico de doenças a partir de imagens microscópicas obtidas por meio de amostras em exames convencionais de biópsia. A utilização de técnicas de processamento computacional de imagens possibilita a identificação de elementos e a extração de características, o que contribui com o estudo da organização estrutural dos tecidos e de suas variações patológicas, levando a um aumento da precisão no processo de tomada de decisão. Os conceitos e técnicas envolvendo redes complexas são recursos valiosos para o desenvolvimento de métodos de análise estrutural de componentes em imagens médicas. Dentro dessa perspectiva, o objetivo geral deste trabalho foi o desenvolvimento de um método capaz de realizar o processamento e a análise de imagens obtidas em exames de biópsias de tecidos de pólipo de cólon para classificar o grau de atipia da amostra, que pode variar em: sem atipia, baixo grau, alto grau e câncer. Foram utilizadas técnicas de processamento, incluindo um conjunto de operadores morfológicos, para realizar a segmentação e a identificação de estruturas glandulares. A seguir, procedeu-se à análise estrutural baseada na identificação das glândulas, usando técnicas de redes complexas. As redes foram criadas transformado os núcleos das células que compõem as glândulas em vértices, realizando a ligação dos mesmos com 1 até 20 arestas e a extração de medidas de rede para a criação de um vetor de características. A fim de avaliar comparativamente o método proposto, foram utilizados extratores clássicos de características de imagens, a saber, Descritores de Haralick, Momentos de Hu, Transformada de Hough, e SampEn2D. Após a avaliação do método proposto em diferentes cenários de análise, o valor de acurácia geral obtida pelo mesmo foi de 82.0%, superando os métodos clássicos. Conclui-se que o método proposto para classificação de imagens histológicas de pólipos utilizando análise estrutural baseada em redes complexas mostra-se promissor no sentido de aumentar a acurácia do diagnóstico de câncer colorretal / According to the National Cancer Institute (INCA), colorectal cancer is the third most common cancer among men and the second most common cancer among women. Currently the main method used for the diagnosis of diseases from microscopic images obtained through samples in conventional biopsy tests are the visual evaluation made by a pathologist. The use of computational image processing techniques allows the identification of elements and the extraction of characteristics, which contributes to the study of the structural organization of tissues and their pathological variations, leading to an increase of precision in the decision making process. Concepts and techniques involving complex networks are valuable resources for the development of structural analysis methods of components in medical images. In this perspective, the general objective of this work was the development of a method capable of performing the image processing and analysis obtained in biopsies of colon polyp tissue to classify the degree of atypia of the sample, which may vary in: without atypia, low grade, high grade and cancer. Processing techniques including a set of morphological operators, were used to perform the segmentation and identification of glandular structures. Next, structural analysis was performed based on glands identification, using complex network techniques.The networks were created transforming the core of the cells that make up the glands in vertices, making the connection of the same with 1 to 20 edges and the extraction of network measurements to create a vector of characteristics. In order to comparatively evaluate the proposed method, classical image characteristic extractors were used, namely, Haralicks Descriptors, Hus Moments, Hough Transform, and SampEn2D. After the evaluation of the proposed method in different analysis scenarios, the overall accuracy value obtained by it was 82.0%, surpassing the classical methods. It is concluded that the proposed method for the classification of histological images of polyps using structural analysis based on complex networks is promising in order to increase the accuracy of the diagnosis of colorectal cancer

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

<p>The technique of volume rendering can be a powerful tool when visualizing 3D medical data sets. Its characteristic of capturing 3D internal structures within a 2D rendered image makes it attractive in the analysis. However, the applications that implement this technique fail to reach out to most of the supposed end-users at the clinics and radiology departments of today. This is primarily due to problems centered on the design of the Transfer Function (TF), the tool that makes tissues visually appear in the rendered image. The interaction with the TF is too complex for a supposed end-user and its capability of separating tissues is often insufficient. This thesis presents methods for detecting the regions in the image volume where tissues are contained. The tissues that are of interest can furthermore be identified among these regions. This processing and classification is possible thanks to the use of a priori knowledge, i.e. what is known about the data set and its domain in advance. The identified regions can finally be visualized using tissue adapted TFs that can create cleaner renderings of tissues where a normal TF would fail to separate them. In addition an intuitive user control is presented that allows the user to easily interact with the detection and the visualization.</p>

The technique of volume rendering can be a powerful tool when visualizing 3D medical data sets. Its characteristic of capturing 3D internal structures within a 2D rendered image makes it attractive in the analysis. However, the applications that implement this technique fail to reach out to most of the supposed end-users at the clinics and radiology departments of today. This is primarily due to problems centered on the design of the Transfer Function (TF), the tool that makes tissues visually appear in the rendered image. The interaction with the TF is too complex for a supposed end-user and its capability of separating tissues is often insufficient. This thesis presents methods for detecting the regions in the image volume where tissues are contained. The tissues that are of interest can furthermore be identified among these regions. This processing and classification is possible thanks to the use of a priori knowledge, i.e. what is known about the data set and its domain in advance. The identified regions can finally be visualized using tissue adapted TFs that can create cleaner renderings of tissues where a normal TF would fail to separate them. In addition an intuitive user control is presented that allows the user to easily interact with the detection and the visualization.