document

Deep Learning Enhanced Contrast Source Inversion And Phase Error Based Conductivity Correction For Electrical Properties Tomography: Exploration Of Physics-Assisted Deep Learning Methodology For Magnetic Resonance Based Electrical Properties Tomography

Edelman, Jory (author)

Magnetic resonance electrical properties tomography is a type of quantitative magnetic resonance imaging that aims to reconstruct the conductivity and permittivity of biological tissue. These electrical properties of the tissue can be used to compute the specific absorption rate, to differentiate tumours from healthy tissue and for hyperthermia...

master thesis 2023

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Identification of Large-Scale Structures in Turbulence

Ramanna, Austin (author)

This thesis aims to automatically and reliably detect large-scale structures in turbulent flows. To achieve this, a U-net (a type of neural network) is trained using artificially generated data. From the network output, continuous structures are derived and general statistics, including, volume fraction, relative kinetic energy and length scales...

master thesis 2023

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Semantically-Guided 3D Building Facade Reconstruction: A Learning-Based MVS Approach

Panagiotidou, Ioanna (author)

This thesis introduces a Learned-Based Multi-View Semantic Stereo method, addressing the limitations of traditional and learned-based Multi-View Stereo (MVS) techniques in reconstructing reflective and low-textured regions, particularly prevalent in 3D models of buildings. Traditional methods lack completeness, while learned-based methods...

master thesis 2023

document

Amyloid-beta plaque quantification and analysis

de Vries, Chiel (author)

Alzheimer's disease (AD) is becoming more prevalent as the world population gets older. The formation of Amyloid-beta (\AB) plaques is one of the pathologies related to AD. Recent work has shown that the \ab load in brain tissue has a negative correlation with cognitive performance in cognitively healthy centenarians. <br/>This work aims to...

master thesis 2023

document

Correcting Global Elevation Models for Canopy and Infrastructure Using a Residual U-Net

Dong, Haoyang (author)

Digital Surface Models (DSMs) are commonly employed to investigate topographical characteristics and processes; however, the presence of canopy and infrastructure in urban and forested areas can lead to height biases and inaccuracies. In this study, I aim to correct such biases by applying a deep learning approach known as Residual U-Net to...

master thesis 2023

document

Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach

Therias, Adele (author)

The production of cocoa beans contributes to 7.5% of European Union (EU) driven deforestation. For this reason, the recent European Union Deforestation-free Regulation (EUDR) requires producers to perform comprehensive tracking of cocoa farm extents. However, cocoa crops present unique detection challenges due to their complex canopy structure,...

master thesis 2023

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Machine learning for post-storm profile predictions: Using XBeach and convolutional neural network structure U-Net to predict 1D dune erosion profile shapes at the Holland Coast

van Asselt, Koen (author)

To reduce computational efforts, surrogate models have been developed for dune erosion prediction. Current surrogate models can describe the relationship between the XBeach input and output (Athanasiou, 2022) and provides a prediction of a morphological indicator based on a parameterized input (profile shape parameters and hydrodynamics). In...

master thesis 2023

document

Deep Learning for Post-Contrast T1-Weighted Brain MRI Synthesis

van Oosterhoudt, Ruben (author)

Introduction: Magnetic Resonance Imaging is a commonly used technique for the initial diagnosis of gliomas. T1, T2, T2-FLAIR, and post-contrast T1 with gadolinium-based contrast agents (GBCAs) can show tumor characteristics. However, using this contrast agent poses a risk to patients with kidney failures, has environmental impact, and increases...

master thesis 2023

document

Algal Bloom Forecasting: Classical Machine Learning versus Deep-Learning

Lubbers, Rob (author)

The aim of this paper is to find out which Machine Learning (ML) model predicts the concentration of Chlorophyll-a, in the Palmar lake in Uruguay best. Currently there are no such models to predict the growth in this lake. The algorithms which will be compared in this paper are a Linear Regression model and the U-Net model. We will compare the...

bachelor thesis 2023

document

Automated rooftop solar panel detection through Convolutional Neural Networks

Pena Pereira, Simon (author)

Transforming the global energy sector from fossil-fuel based to renewable energy sources is key to limiting global warming and efficiently achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to install photovoltaic (PV) systems on their rooftops. In this context, planning an efficient...

master thesis 2023

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Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

Bafti, Alireza Ghaderi (author), Ahmadi, Arman (author), Abbasi, A. (author), Kamangir, Hamid (author), Jamali, Sadegh (author), Hashemi, Hossein (author)

Actual evapotranspiration (ET_a) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ET_a is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ET_a values,...

journal article 2023

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A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning

de Roda Husman, S. (author), Lhermitte, S.L.M. (author), Bolibar, J. (author), Izeboud, M. (author), Hu, Zhongyang (author), Shukla, S. (author), van der Meer, Marijn (author), Long, David (author), Wouters, B. (author)

While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed...

journal article 2023

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Surrogate convolutional neural network models for steady computational fluid dynamics simulations

Eichinger, Matthias (author), Heinlein, A. (author), Klawonn, Axel (author)

A convolution neural network (CNN)-based approach for the construction of reduced order surrogate models for computational fluid dynamics (CFD) simulations is introduced; it is inspired by the approach of Guo, Li, and Iori [X. Guo, W. Li, and F. Iorio, Convolutional neural networks for steady flow approximation, in Proceedings of the 22nd ACM...

journal article 2022

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Infrared thermal defect identification and reconstruction of artworks using a spatiotemporal deep neural network

Moradi, M. (author), Ghorbani, R. (author), Sfarra, Stefano (author), Tax, D.M.J. (author), Zarouchas, D. (author)

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of the artworks while avoiding the loss of any precious materials that make it up. The use of Infrared Thermography (IRT) is an interesting concept since surface and subsurface faults can be...

conference paper 2022

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Semantic segmentation of historical photographs of the Antarctica peninsula

Dahle, F. (author), Tanke, Julian (author), Wouters, B. (author), Lindenbergh, R.C. (author)

A huge archive of historical images of the Antarctica taken by the US Navy between 1940 and 2000 is publicly available. These images have not yet been used for large-scale computer-driven analysis as they were captured with analog cameras. They were only later digitized and contain no semantic information. Most modern deep-learning based...

journal article 2022

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Landslide detection and mapping on Synthetic Aperture Radar amplitude satellite imagery

Machielse, Marja (author)

There is a growing demand for detailed and accurate landslide maps and inventories worldwide. The mapping of landslides is essential for emergency response, disaster mitigation and a better understanding of landslides. Currently, it is still difficult to detect the timing and extent of landslides accurately through satellite imagery. Most often...

master thesis 2021

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Exploring deep learning to improve allelic peak calling in forensic DNA analysis

van Belkom, Myrte (author)

When processing a trace DNA sample at the Netherlands Forensic Institute, an STR electropherogram can be created. An analyst uses this electropherogram and analysis software to read out peaks signifying DNA. After analysis, the DNA profile is used in the interpretation process, which can include the comparison to a reference DNA profile of a...

master thesis 2021

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Automated segmentation of lacunes of presumed vascular origin in brain MRI scans

Ooms, Eline (author)

Lacunes of presumed vascular origin (lacunes) are small lesions in the brain and are an important indicator of cerebral small vessel disease (cSVD). To gain more insight in this disease, obtaining more information about the shape, size and location of lacunes is essential. However, manual segmentation (the voxel-wise labeling of lacunes in a...

master thesis 2021

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Automating malaria diagnosis: a machine learning approach: Erythrocyte segmentation and parasite identification in thin blood smear microscopy images using convolutional neural networks

van Driel, Noor (author)

Reliable malaria diagnosis techniques that are suitable for point-of-care testing in high burden areas, are vital for effective treatment and monitoring of the disease. Identification of malaria parasites in Giemsa stained blood slides is currently the most widely accepted technique, but its availability is limited by the need for highly trained...

master thesis 2020

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Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation

Vigueras Guillén, J.P. (author), Sari, B. (author), Goes, S.F. (author), Lemij, Hans G. (author), van Rooij, Jeroen (author), Vermeer, K.A. (author), van Vliet, L.J. (author)

Background<br/><br/>Corneal endothelium (CE) images provide valuable clinical information regarding the health state of the cornea. Computation of the clinical morphometric parameters requires the segmentation of endothelial cell images. Current techniques to image the endothelium in vivo deliver low quality images, which makes automatic...

journal article 2019