@thesis{PB-VRVis-2020-024,
author = {Neubauer, Theresa},
title = {Volumetric Tumor Segmentation on Multimodal Medical Images using Deep Learning},
year = {2020},
type = {Master´s Thesis},
institution = {Fakult{\"a}t f{\"u}r Informatik, TU Wien},
subtitle = {Master's Thesis},
url = {https://www.vrvis.at/publications/PB-VRVis-2020-024},
abstract = {The automatic segmentation of tumors on different imaging modalities supports medical
experts in patient diagnosis and treatment. Magnetic resonance imaging (MRI), Computed Tomography (CT), or Positron Emission Tomography (PET) show the tumor in
a different anatomical, functional, or molecular context. The fusion of this multimodal
information leads to more profound knowledge and enables more precise diagnoses. So
far, the potential of multimodal data is only used by a few established segmentation
methods. Moreover, much less is known about multimodal methods that provide several
modality-specific tumor segmentations instead of a single segmentation for a specific
modality.
This thesis aims to develop a segmentation method that uses the multimodal context to
improve the modality-specific segmentation results. For the implementation, an artificial
neural network is used, which is based on a fully convolutional neural network. The
network architecture has been designed to learn complex multimodal features to predict
multiple tumor segmentations on different modalities efficiently.
The evaluation is based on a dataset consisting of MRI and PET/CT scans of soft tissue
tumors. The experiment investigated how different network architectures, multimodal
fusion strategies, and input modalities affect the segmentation result. The investigation
showed that multimodal models lead to significantly better results than models for single
modalities. Promising results have also been achieved with multimodal models that
segment several modality-specific tumor contours simultaneously.},
}