Bladder cancer (BCa) is the fourth most commonly diagnosed cancers in men and the eighth most common in women. It is an abnormal growth of tissues which develops in the bladder lining. Histological analysis of bladder tissue facilities diagnosis as well as it serves as an important tool for research. To bet- ter understand the molecular profile of bladder cancer and to detect predictive and prognostic features, microscopy methods, such as immunofluorescence (IF), are used to investigate the characteristics of bladder cancer tissue. For this project, a new method is proposed to segment cancer epithelial us- ing nuclei morphology captured with IF staining. The method is implemented using deep learning algorithms and performance achieved is compared with the literature. The dataset is stained for nuclei (DAPI) and a marker for cancer epithelial (panEPI) which was used to create the ground truth. Three popu- lar Convolutional Neural Network (CNN) namely U-Net, Residual U-Net and VGG16 were implemented to perform the segmentation task on the tissue mi- croarray dataset. In addition, a transfer learning approach was tested with the VGG16 network that was pre-trained with ImageNet dataset. Further, the performance from the three networks were compared using 3fold cross-validation. The dice accuracies achieved were 83.32% for U-Net, 88.05% for Residual U-Net and 82.73% for VGG16. These findings suggest that segmentation of cancerous tissue regions, using only the nuclear morphol- ogy, is feasible with high accuracy. Computer vision methods better utilizing nuclear morphology captured by the nuclear stain, are promising approaches to digitally augment the conventional IF marker panels, and therefore offer im- proved resolution of the molecular characteristics for research settings.

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.

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.

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.

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.

Melanoma is one of the deadliest forms of cancer. Unfortunately, its incidence rates have been increasing all over the world. One of the techniques used by dermatologists to diagnose melanomas is an imaging modality called dermoscopy. The skin lesion is inspected using a magnification device and a light source. This technique makes it possible for the dermatologist to observe subcutaneous structures that would be invisible otherwise. However, the use of dermoscopy is not straightforward, requiring years of practice. Moreover, the diagnosis is many times subjective and challenging to reproduce. Therefore, it is necessary to develop automatic methods that will help dermatologists provide more reliable diagnoses.  Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. Recent developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in the clinical diagnostic ability to the point that it can detect melanoma in the clinic at the earliest stages. This technology’s global adoption has allowed the accumulation of extensive collections of dermoscopy images. The development of advanced technologies in image processing and machine learning has given us the ability to distinguish malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow earlier detection of melanoma and reduce a large number of unnecessary and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, a widespread implementation must await further technical progress in accuracy and reproducibility.  This thesis provides an overview of our deep learning (DL) based methods used in the diagnosis of melanoma in dermoscopy images. First, we introduce the background. Then, this paper gives a brief overview of the state-of-art article on melanoma interpret. After that, a review is provided on the deep learning models for melanoma image analysis and the main popular techniques to improve the diagnose performance. We also made a summary of our research results. Finally, we discuss the challenges and opportunities for automating melanocytic skin lesions’ diagnostic procedures. We end with an overview of a conclusion and directions for the following research plan.

This report introduces the multislice method for modeling the interaction between an electron and the atoms in the specimen (electron-specimen interaction). The multislice method is an approximation to the full quantum mechanical model for this interaction. After introducing the theory, we discuss how the multislice method is implemented and integrated into TEM-simulator, a software for simulation of Transmission Electron Microscope (TEM) images.

Single photon emission tomography (SPECT) data from myocardial perfusion imaging (MPI) are normally displayed as a set of three slices orthogonal to the left ventricular (LV) long axis for both ECG-gated (GSPECT) and non-gated SPECT studies. The total number of slices presented for assessment depends on the size of the heart, but is typically in excess of 30.  A requirement for data presentation is that images should be orientated about the LV axis; therefore, a set of radial slice would fulfill this need. Radial slices are parallel to the LV long axis and arranged diametrically. They could provide a suitable alternative to standard orthogonal slices, with the advantage of requiring far fewer slices to adequately represent the data. In this study a semi-automatic method was developed for displaying MPI SPECT data as a set of radial slices orientated about the LV axis, with the aim of reducing the number of slices viewed, without loss of information and independent on the size of the heart. Input volume data consisted of standard short axis slices orientated perpendicular to the LV axis chosen at the time of reconstruction.  The true LV axis was determined by first determining the boundary on a central long axis slice, the axis being in the direction of the y-axis in the matrix. The skeleton of the myocardium were found and the true LV axis determined for that slice. The angle of this axis with respect to the y-axis was calculated. The process was repeated for an orthogonal long axis slice. The input volume was then rotated by the angles calculated. Radial slices generated for presentation were integrated over a sector equivalent to the imaging resolution (1.2 cm); assuming the diameter of the heart is about 8cm then non-gated data could be represented by 20 radial slices integrated over an 18 degree section. Gated information could be represented with four slices spaced at 45 intervals, integrated over a 30 degree sector.

In contemporary IT industry, Multi-tasking solutions are highly regarded as optimal solutions, because hardware is equipped with multi-core CPUs.  With Multi-Core technology, CPUs run with lower frequencies while giving same or better performance as a whole system of processing. This thesis work takes advantage of multi-threading architecture in order to run different tasks under different cores such as SIP signaling and messaging to establish one or more SIP calls, capture voice, medical data, and packetize them to be streamed over internet to other SIP agents. VoIP is designed to stream voice over IP. There is inter-protocol communication and cooperation such as between the SIP, SDP, RTP, and RTCP protocols in order to establish a SIP connection and- afterwards- stream media over the internet. We use the Microsoft COM technology in order to better the C++ component design. It allows us to design and develop code once and run it anywhere on different platforms. Using VC++ helps us reduce software design time and development time. Moreover, we follow software design standards setup by software engineers’ society. VoIP technology uses protocols such as the SIP signaling protocol to locate the user agents that communicate with each other. Pjsip is a library that allows developers to extend their design with SIP capability. We use the PJSIP library in order to sign up our own developed VoIP module to a SIP server over the Internet and locate other user agents. We implement and use the already-designed iRTP protocol instead of the RTP to stream media over the Internet. Thus, we can improve RTP packet delays and improve Quality of Service (QoS). Since medical data is critical and must not be lost, the iRTP guarantees no loss of medical data. If we want to stream voice only, we would not need iRTP, because RTP is a good protocol for voice applications. Due to the increasing Internet traffic, we need to use a reliable protocol that can detect packet loss of medical data. iRTP resolves the issue and leverages QoS. This thesis work focuses on streaming medical data and medical voice-calls using VoIP, even over small bandwidths and in high traffic periods. The main contribution of this thesis is in the parallel design of iRTP and the implementation of this very design in order to be used with Multi-Core technology. We do so via multi-threading technology to speed up the streaming of medical data and medical voice-calls. According to our tests, measurements, and result analyses, the parallel design of iRTP and the multithreaded implementation on VC++ leverage performance to a level where the average decrease in delay is 71.1% when using iRTP for audio and medical data instead of the nowadays applied case of using an RTP stream for audio and multiple TCPs streams for medical data .