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
  • 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)