Master End Projects
- Celine Schaus
“Accelerating compressed sensing in MRI imaging reconstructions using a preconditioner” - Bas Dille
“Advanced Image Analysis Tools for light sheet microscopy” - Tjeerd Peters
“Deep learning methods for replication fork detection in EM images“ - Tijmen de Wolf
“Deep learning methods for motion analysis of cardiomyocytes“ - Hein Zijlstra
“Single particle tracking analysis: from statistical tools to novel machine learning methods“
(2021, Sept, Erasmus MC) - Merve Gunes
“A machine learning assessment of multi-resolution remote sensing data for Natura 2000 dune habitat classification“
(2020, Oct. 2, TU Delft) - Fokke Johannes Dijkstra
“Breakpoint detection through neural nets“
(2019 TU Delft) - Manuel Huber
“Development of a Deep Learning Surrogate Model to Simulate MetOp ASCAT Observations with Land Surface Parameters“
(2019, TU Delft) - Pim Klaassen
“Using Neural Networks to Model Behavior in Vessel Trajectories”
(2019, TU Delft) - Renske Taylor
“Development of a 3D Image Analysis Method to Measure Blast-Induced Fragmentation at the Leveäniemi Mine“
(2019, TU Delft) - Konstantinos Chatzopoulos Vouzoglanis
“Eutrophication prediction in the Dutch coastal waters using remote sensing data and machine learning“
(2019, TU Delft) - Konstantinos Vlachos
“Investigation of meso-scale Sentinel-3 product along-track correlations and the potential of inter-track SSHA estimation using machine learning“
(2019, TU Delft) - Tom Sassen
“The influence of drone flightpath on photogrammetric model quality“
(2019 TU Delft) - Marloes Arts
“BRCA2 mobility analysis using Deep Learning and the Moment Scaling Spectrum“
(July 2018, TU Delft / EMC)
Bachelor End Projects
- Boyd Peters
“3D simulation of BRCA2 proteins and clustering improvement of Noise2Noise SPT“
(running) - Thies Caljé
“Survival analysis of acute myeloid leukemia patients using deep learning and AI“ - Stijn Karaçoban
“DGFR-β detection and an automated method for cardiomyocyte classification”
(2021, Sep. TU Delft / EMC) - Philippe Antoine Henry
“Noise2Noise as a microscopy denoising method“
(2021, Jul. TU Delft / EMC) - Arielle Molina Rakos
“Defining Classes and Classification of stained MSC cells with Deep Learning“
(2021, May, TU Delft / EMC) - Maud Diepeveen
“Quantitative analysis of diffusive transitions of DNA repair proteins using Markov Chain methods“
(Sept., 2020, TU Delft / EMC) - Tom van de Kamp
“Contraction Analysis Method Optimization for Cardiomyocytes and Vascular Smooth Muscle Cells (VSMCs)“
(Aug. 2020, TU Delft / EMC) - Rana Sannia Ul Haq
“Comparing different methods to analyse the diffusive be- haviour of DNA repair proteins by single-particle tracking“
(July, 2017, TU Delft / EMC)
Bachelor End Projects (co-supervised)
- Matthijs van Driessche
“In vitro analysis of cardiac fibrosis and automated heart segmentation”
(2022, July, TU Delft / EMC) - Matthijs Jansen
“Artificial Intelligence for Lung Image Analysis”
(2022, July, TU Delft / EMC) - Hugo Buitelaar
“An Automated Image Analysis Pipeline for Cardiac Function in Micro-CT Mouse Scans”
(2022, Jan. TU Delft / EMC) - Daan te Rietmole
“Teaching AI platelet age”
(2021, June, TU Delft / EMC)
Research Projects
- Dalia Aljawaheri
- Larissa Lobbezoo
- Daphne Laan (co-supervising)
“QUANTIFYING FIBRIN NETWORKS: A systematic study of automated image analysis methods“
(Jan. 2022, TU Delft / EMC) - David Garcia vanBijsterveld (co-supervising)
“Cancer Cell Tracking using Deep Learning“
(July, 2021, TU Delft / EMC) - Tijmen de Wolf
“Deep learning based segmentation of 53BP1 foci“
(July, 2020, TU Delft / EMC) - Kirsten van Kooij
“Analysis of microCT 3D images“
(Sep., 2020, TU Delft / EMC)
