TSNE is a popular technique for visualizing high-dimensional data. It finds a low-dimensional representation of the data, also known as embedding, by optimizing a highly non-linear cost function. The optimization process is done iteratively, often with first-order methods such as gradient descent (GD). The performance of iterative optimization procedures depends on the time that it takes to execute each iteration and the number of iterations that are required to converge to an optimum. In the context of tSNE, different methods that tackle these issues have appeared, but, despite their success, they are still limited by their separate development. For instance, BH-SNE, one of the most popular variants of tSNE, uses the Barnes-Hut algorithm to accelerate each iteration, but still uses GD, which needs a large number of them to converge. On the other hand, quasi-Newton optimization methods are effective at reducing the number of iterations, but available implementations are constrained by the additional per-iteration cost. In our work, we attempted to reconcile these two research branches. For this, we investigated the usage of Line Search (LS) and Trust Region (TR) based quasi-Newton methods for tSNE and improved their performance by identifying and addressing several of their bottlenecks. Among the different aspects of quasi-Newton optimization for tSNE that we covered were the selection of a suitable Hessian approximation, the usage of approximate gradients and cost function evaluations, and the choice of linear solver, which the algorithm uses to find the next iterate. The resulting techniques, which we denote SD-LS-CG and SD-TR-CG, have a lower per-iteration cost than alternative quasi-Newton methods and converge in fewer iterations than the traditional implementation of tSNE, which uses a highly tuned version of GD. The results suggest that these quasi-Newton methods could be a better alternative to fast tSNE implementations such as BH-SNE, given their overall faster convergence. Furthermore, we observed that SD-LS-CG was able to consistently avoid local optima if initialized with PCA, suggesting that the choice of Hessian approximation can have a similar role to the early exaggeration phase in tSNE.

Narcolepsy is a chronic neurological condition that results from the dysregulation of the sleep-wake cycle occurring in an early stage, specifically in adolescence. Patients with narcolepsy experience excessive daytime sleepiness, cataplexy, hypnagogic hallucinations, sleep paralysis and disturbed nocturnal sleep and these symptoms together form the narcolepsy symptom pentad. However, the symptoms related to narcolepsy are not limited to the pentad and cover a broad range of other symptoms, some of which are not directly related to sleep, like, increase in weight, binge eating, anxiety, agitation. Therefore, researchers from sleep medicine center, Kempenhaeghe wanted to understand how a selected set of 20 symptoms are related to narcolepsy.


In this thesis we present a visual analytics framework to help the researchers understand these symptoms in relation to narcolepsy and identify patterns among them. Using relevant attributes interesting population subsets are formed and the results are compared. The thesis comprises of three main tasks, the first being visualizing the distribution of individual attributes of patients in which a symptom is present and not present and for severity level. Then, pairs of symptoms are visualized based on their agreement and association to identify groups of related symptoms and trends present. Lastly, multiple symptoms are visualized to identify patterns amongst them.


The visual analytics framework was implemented and evaluated through a user study. The study showed that the visualization developed was useful in gaining new insights to form interesting hypothesis along with possible extensions for future development. This is the first step towards an extensive visual analytics framework in the study of narcolepsy symptom spectrum.

Nowadays video plays an important role in the coaching of athletes across many different sports. To make more use of the advantages videos can provide for coaching, the Dutch Sailing team is shifting from manually recording short videos towards continuously recording training sessions. This new recording approach provides opportunities and creates challenges at the same time. In this thesis we present a pipeline to address the problems with the stability of the recording and the first steps towards a Visual Analysis Framework, which leverages the available video data. New information is extracted from the video recordings by detecting and tracking the boat and sailors. Moreover, we semi-automatically highlight interesting intervals in time of a recorded training session. These are the first steps towards an extensive Visual Analysis Framework which has the potential to make the analysis of the videos easier and provide the coaches with tools to improve the analysis of the performance of the sailors.

Heart and vessel diseases, or cardiovascular diseases (CVDs), are globally the main cause of mortality and morbidity. The blood flow plays an important role in their occurrence and progression. Therefore, knowledge of the blood flow is of key importance to reduce and threat these diseases. This knowledge requires both high-quality data and an insightful visual representation. Using advanced imaging techniques, for example phase-contrast magnetic resonance imaging (PC-MRI), the blood flow in a patient can be measured. This technique provides patient-specific 3D data over time, that is, for every measured position, a so-called voxel, the velocity of the blood is measured in all three directions. From these three values per voxel, a vector can then be reconstructed that tells us how fast and in which direction the blood was flowing at the measured location. By doing this for multiple moments one can obtain this data throughout a heart cycle. Since this data is three dimensional and changing over time generating a visual representation of this data is challenging for multiple reasons. One such reason is occlusion where part of the visualization is hidden by the rest of the visualization. Another is visual clutter where the visualization is ``too busy'' and therefore unclear. Moreover, the measured data is subject to noise and artefacts which further hinder the visualization. In this dissertation, novel visualization approaches are presented that address these and other visualization challenges of PC-MRI data. Besides PC-MRI data, blood flow data can be created using computer simulation models, for example using computational fluid dynamics (CFD) models, that are based on physical models. For this usually the shape of the blood vessel is measured using imaging techniques which is in turn used to simulate the blood flow inside the vessel. However, both measuring and modelling the blood flow have their own advantages and disadvantages. For example, PC-MRI measurements suffer from the inevitable effects of measurement noise which causes the data to deviate from the actual blood flow in the patient. Simulations, on the other hand, require detailed input information and are based on model assumptions, and hence result in data that is not always representative for the patient, however, the resulting data does correspond to the physical model. In addition to visualization approaches, this work also presents novel methods that combine PC-MRI measurements and simulations such that the resulting data is both physically-plausible and patient-specific. @en

Gamification and game-based learning have received wide attention in the past few decades. By blending game design and mechanics into traditional learning environment, they enhance students’ participation, motivation and engagement. Squla is such a gamified learning platform where we can find game components like coins, virtual shop, competitive activities and collectables. Besides the above gamified elements, Squla has also transformed standard questions into fun games such as shooting catapults and clicking the popping bubbles. These games are designed to further engage the students and improve their learning.
In this project, we analysed player game type preference based on their game log data, and measure the impact of customised game type delivery. We targeted education group 4 and 5 students users, and focus on catapult games and bubble popper games as they are the most played. A set of features that could reflect students’ preference and emotion states are selected and analysed, including correct ratio, playtime, quitting possibility, etc. Using data clustering, we group students who have similar behaviour and predict their preferred game types. We identified three group of students, one shows high completion rate on all forms of questions, another shows rather low overall completion rate, and the last group has rather high completion rate on bubble popper games and lower completion rate on the catapult shooting games. Based on such findings, we conducted experiment on them to look into different gaming contents’ impact on their learning and engagement. A final experiment consists of a short math quiz and a follow-up questionnaire. The two-week online experiment receives 91 valid responses. Post-play questionnaire, as well as the game log suggest different contents could affect students’ engagement. In particular, preferred contents can elevate a sense of happiness and enhance perceived learning.

Coronary artery disease is the most common type of heart disease, which influences 110 million people's health and causes 8.9 million deaths in 2015. Physicians can visualize the lesion in coronary arteries by cardiac angiography (X-ray image) during diagnosis and treatment of coronary artery disease. The pathological findings in cardiac angiography are reported per segment or per artery of the coronary artery tree, therefore, it requires to annotate the name of each segment or artery in the coronary artery tree.

This thesis proposes a data-driven method as a first attempt at annotating cardiac angiography based on deep learning. The method aims at automatically regressing segment points between different segments on the coronary artery tree as the annotation of the cardiac angiography. The proposed data-driven cardiac angiography annotation methods can learn and generalize from manually annotated cardiac angiography examples, but its performance is limited by the number and quality of examples for learning.

Ablation is a medical procedure to treat liver cancer where a needle-like catheter has to be inserted into a tumor, which will then be heated or frozen to destroy the tumor tissue. To guide the catheter, Ultrasound(US) imaging is used which shows the catheter position in real time. However, some tumors are not visible on US images. To make these tumors visible, image fusion can be used between the inter-operative US image and a pre-operative contrast enhanced CT(CECT) scan, on which the tumors are visible. Several methods exist for tracking the motions of the US transducer relative to the CECT scan, but they all require a manual initialization or external tracking hardware to align the coordinate systems of both scans. In this thesis we present a technique for finding an initialization using only the image data. To achieve this, deep learning is used to segment liver vessels and the boundary of the liver in 3D US images. To find the rigid transformation parameters, the SaDE evolutionary algorithm was used to optimize the alignment between the blood vessels and the liver boundary between both scans.

2D/3D registration is the process of aligning 3D volume data to a 2D image. Typically, during orthopedic trauma surgeries, 3D volume data is acquired prior to the surgery to visualize the extent of the injury and also plan the operation. During the surgery, 2D fluoroscopic images are acquired to visualize the position of surgical instruments and understand the progress of the operation. However, the depth perception from these 2D images becomes difficult. By employing 2D/3D registration to overlay pre-operative imaging modalities, doctors may be presented a better way to visualize the progress of the surgery. Although several approaches have been proposed in the literature to deal with 2D/3D registration, they are limited by the need for a good initial alignment. This requirement for accurate initial alignment is the main reason hindering the adoption of 2D/3D registration in clinical practice. In this thesis, we propose automated initialization strategies, the output of which can be used by any common 2D/3D registration algorithm for
further fine tuning.

A library based approach is to used to study the initialization problem. Four parameters of the six dimensional parameter space are sampled and their projections are stored prior to the surgery. We proposed frequency and feature based methods to retrieve the correct library match which is used for the initialization during the surgery. The feasibility of the approach is demonstrated by initializing an intensity based 2D/3D registration method with the automatically obtained estimation of the initialization transformation parameters. For fixed C-arm cases with available ground truth the initialization was successful for 87:41% for various anatomies such as the head, pelvis, spine, knees and feet. We further tested the approach in a completely un-calibrated scenario using a mobile C-arm. Visual evaluation of the results revealed a success rate of 88:4% for the initialization. Successful results were also obtained even in the presence of additional surgical instruments indicating the robustness of the proposed approach.

Touchless interaction has recently gained considerable attention by researchers as well as industry. Different domains are interested in implementing this technology in their solutions. Medical visualization has a special interest in this technology due to the sterile conditions in operating rooms. Exploration and detailed inspection of the scanned objects are among the most common interactions performed by professionals. These operations become more challenging when combined with touchless input. Context-aware methods exist, which facilitate navigation, but these methods are made for meshes and not for volume renderings. Hence the research question: Can these methods be extended to volume renderings and how well will they perform with touchless interaction metaphors? Metaphor and underlying VolCam algorithm are presented in this work. The metaphor allows users to perform exploration and inspection tasks on medical volume data using touchless input device - LeapMotion. The VolCam - an extension of the ShellCam algorithm, automatically maps the user input to distinct camera movements based on the current scene view by sampling the visible part of the volume. Interactive frame rates are achieved by performing computations on GPU. No pre-processing or specialized data structures are required which makes the technique directly applicable to wide-range of volume datasets.

The explosion of video data in surveillance calls for large amount of annotated datasets that could be used for information retrieval, learning-based network training and algorithms evaluation phase. A number of annotated video
datasets have been shared to public, however, these open annotated datasets lack spatio-temporal information which can contribute to motion analysis researches. Moreover, manual annotation is tedious in lengthy frame sequences
where the numbers are overwhelming. In this work we propose a visualization solution to facilitate video data retrieval, mining and annotation. It works as an integrated visual analytics system which supports collecting moving object samples and studying motion patterns in video, with the facilitation of video data visualization, using various spatial and/or temporal features, filtering parameters and similarity measuring models. The annotation output can be
applied to multiple video analysis researches. In this paper, we present the system and propose a workflow for costeffective video annotation of spatio-temporal data as well as facilitating comprehension of video data.