Deep Learning for Landmark Detection, Segmentation, and Multi-Objective Deformable Registration in Medical Imaging

Research output: ThesisDissertation (TU Delft)

22 Downloads (Pure)

Abstract

Cervical cancer affects about half a million women globally every year. The treatment of cervical cancer with the aim of healing mainly consists of surgery, radiation treatment, or a combination of radiation treatment with chemotherapy or hyperthermia. Radiation treatment is a type of treatment wherein a high dose of ionizing radiation is used to kill the tumor cells. The radiation dose is usually delivered in the form of External Beam Radiation Treatment (EBRT) with a linear accelerator followed by internal radiation treatment (brachytherapy) during which a small radioactive source is passed through an applicator and needles that are placed temporarily nearby the cervix. EBRT typically spans several weeks with daily sessions (often referred to as fractions), whereas brachytherapy typically consists of three or four fractions based on one to three implantations. The aim of the radiation treatment is to provide effective radiation to kill the tumor cells while sparing the nearby healthy tissue or Organs At Risk (OARs) as much as possible. This is achieved by treatment planning following the contouring of target volumes and OARs, on medical imaging scans, which typically are Computed Tomography (CT) and/orMagnetic Resonance Imaging (MRI)....
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Bosman, P.A.N., Promotor
  • Alderliesten, T., Copromotor
  • Westerveld, G.H., Copromotor, External person
Award date11 Apr 2025
Electronic ISBNs978-94-6518-026-7
DOIs
Publication statusPublished - 2025

Keywords

  • deep learning
  • deformable image registration
  • cervical cancer
  • radiation treatment
  • organs at risk segmentation
  • landmark detection
  • multi-objective optimization
  • multi-objective learning

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