Flow visualization is an important topic in many scientific domains and has been an active field of research for many years. Many different methods of analysis can be used in order to analyze flow, however recently big progress have been reported on the manifold learning algorithms for high-dimensional data. This thesis investigates the use of Stochastic Neighbor Embedding (SNE) methods, t-distributed stochastic Neighbour embedding(t-SNE) and hierarchical stochastic Neighbor embedding (HSNE) for flow analysis. In this thesis the Manivault ,Veith et al., 2024, software platform has been used in order to create an interactive analysis tool for SNE methods used on flow data. This tool consists of a 3D viewer plugin that visualizes the full path lines and an existing scatterplot plugin that is used in order to interact with the created SNE maps. The experiments and comparisons reported in this thesis aimed to compare the use of t-SNE and HSNE for analysis of flow structures in 4D Flow MRI data. From this, it can be concluded both t-SNE and HSNE are useful for interaction with and analysis of 4D Flow MRI data. t-SNE can best be used in order to explore and analyze flow data in search for flow structures, and comparing flow patterns between subjects. HSNE on the other hand gives a better separation between different flow components, however at the expense of a less accurate preservation of vortices and other flow structures.

With a 5-year survival rate of only 9%, pancreatic cancer is one of the deadliest types of cancer. Among other things, this is caused by the extreme difficulty of diagnosing recurrent pancreatic cancer in an early stage. One of the important next steps in discovering pancreatic cancer automatically on a CT scan is finding healthy pancreatic tissue. This research explores the feasibility of segmenting the pancreas on a CT scan using a deep learning approach, focusing on postoperative cases after pancre- atic resection, by combining state-of-the-art segmentation models for the preoperative pancreas with novel techniques, such as 3D Multi-Scale Convolutional Blocks (MCBs) and KNet architectures. Us- ing pretraining on preoperative data, a complete segmentation pipeline was designed to segment the pancreas in the postoperative state. The experimental results demonstrate that deep learning models can effectively segment the pancreas, despite the increased complexity and difficulty of the postoper- ative state. Notably, employing advanced techniques such as the KNet architecture and MCBs yields significant performance improvements. The newly designed architectures presented in this research, MKNet, MSKNet, and MAKNet, achieve state-of-the-art results for both preoperative and postoperative pancreas segmentation, greatly improving the Hausdorff Distance (HD) and 95th Percentile Hausdorff Distance (HD95) in particular. In the preoperative state, substantial performance improvements were observed compared to the previous state-of-the-art, with a 12.17% increase for HD and a 14.64% in- crease for HD95. Similarly, in the postoperative state, improvements of 11.99% for HD and 13.25% for HD95 were measured. Additionally, qualitative evaluations were conducted with seven experienced radiologists and radiotherapists to validate the algorithm’s performance in a clinical context. Remark- ably, in 83% of the cases, the algorithm was evaluated to accurately segment the pancreas, requiring minimal or no modifications according to the medical experts. This study not only contributes to the state-of-the-art in pancreas segmentation but also introduces comprehensive quantitative and qualitative evaluation methodologies. Furthermore, it addresses the segmentation of the postoperative pancreas, which has not yet been touched upon in previous re- search. Limitations are acknowledged, such as the availability of a limited dataset, and future research directions are proposed, including generalizability and enhancements to the proposed models. Despite the limited availability of data, which impacts the performance and generalization capabilities to some degree, the research in this thesis showcases the capability of the MKNet family architectures to ac- curately segment the postoperative pancreas, offering potential benefits for medical applications, data annotation acceleration, and future research in this domain.

Resting-state fMRI (rs-fMRI) has become an important imaging modality and is commonly used to study intrinsic brain networks. These networks can be obtained by decomposing rs-fMRI data into components, using independent component analysis (ICA). Recently, these ICA components have been used as inputs for neural networks to learn complex relations between the intrinsic networks of the brain and mental disorders or demographic variables. Instead of training a non-linear classifier on these linearly decomposed components, this work asks whether unsupervised representation learning can lead to linearly separable representations for multiple downstream tasks.

We propose to apply non-linear representation learning to voxelwise rs-fMRI data. Learning the non-linear representations is done using two versions of a variational autoencoder (VAE). The first version is a vanilla VAE with 3D residual blocks in both its encoder and decoder. The second version is based on the identifiable VAE and uses a time-dependent prior. The models train to reconstruct the original input data from latent variables it infers. Three predictive models then evaluate the predictive power of the latent variables on an age regression, a sex classification, and a schizophrenia classification task. Each of the predictive models performs predictions for each of the three tasks. The predictive models are a support vector machine (SVM), a k-nearest neighbor (k-NN) model, and a long short-term memory (LSTM) neural network.

We show that our method performs exceptionally well on the age regression and sex classification tasks without any supervision. These results imply that VAEs can model predictive variations in their latent spaces for demographic variables. The models, however, do not do well on the schizophrenia classification task, even when the models are pretrained. Despite the lower performance on the schizophrenia classification task, the overall results are encouraging and pave the way for future work on voxelwise representation learning.

While millions of people world wide suffer from arterial diseases, such as peripheral arterial disease, there are a limited number of methods that can be used to diagnose and track these diseases which are also easy, quick and non-invasive.

This work focuses on what is needed to improve diagnosing and tracking of peripheral arterial disease (PAD) using visualisation techniques. Visualising the blood flow pulsation in the skin can be useful in cases of arterial diseases, as the diseases can influence the blood flow by obstructions and arterial stiffness. The main objective in the visualisation is to show the acceleration of the blood flow, as this is linked to the arterial stiffness.

The proposed algorithm for visualising the acceleration of the blood flow is comprised of multiple steps, including techniques such as motion reduction, Eulerian video magnification, remote photoplethysmography signal extraction and using the second derivative. The input of this algorithm are videos of the skin of patients, this makes this method easy and non-invasive.

Photoplethysmography (PPG) signals are present in videos of skin, but can not be seen with the naked eye. Using Eulerian video magnification the PPG signals are amplified for better processing and visibility. By combining groups of pixels into small patches, a decrease in processing time is achieved and it adds a filtering effect. The size of the patches controls the resolution of the visualisation. Movement from the camera or patient is detrimental when extracting the PPG signal from the video. To counteract the motion in the videos a motion reduction step, using optical flow, is applied. Using the Plane-orthogonal-to-skin (POS) algorithm, the signal extracted from videos is converted to a PPG signal. Calculating the second derivative of the PPG signal gives the acceleration of the signal. By splitting the acceleration signal into positive and negative numbers, the acceleration and deceleration of the blood flow is visualised.

Synthetic videos simulating the skin were generated in various levels of accuracy to aid the development of the algorithm and to conduct experiments. The levels range from a simple pulsating square to a moving PPG signal over a blood vessel like structure. In addition, real videos of patients were used.

The experiments show the feasibility of visualising the acceleration of the blood flow pulsation in the skin, but also highlights areas of improvements and future research. More fine-tuning of the algorithm is needed, in addition to acquiring more videos of patients with PAD before and after surgery in a controlled environment.

A working proof of concept of the algorithm is shown. It has the potential of being a novel method of diagnosing and tracking arterial diseases.

Segmentation of medical images is always a challenge due to complicated anatomical structures and poor image quality. In this paper, aiming to solve dual surfaces segmentation problems, we propose a two-layer levelset method with an efficient distance constraint. With the distance constraint, physical information of wall thickness can be imposed. In our method, a soft distance constraint is designed to promise that unclear boundaries in locations with poor image quality can be detected. Due to our distance constraint, evolution speed of level set function around boundaries can be limited effectively, so that it is much easier for unclear boundaries to be detected. Our method is applied to segment MRI CINE left ventricle (LV) data and MRI SPACE left atrium (LA) data. The accuracy of our method is analyzed by quantitative evaluation methods, which reports that Endocardial average perpendicular distance (APD) and Epicardial APD for LA and LV data are both below or around 1 mm, and the dice similarity coefficients (DSC) are all around 0.95. The evaluation results are within a desirable range. Our method has an excellent performance in both segmenting LA and LV data. Even in locations with poor image quality, our method still performs very well, which proves that our method is flexible and robust.