Diploma and Master Theses (authored and supervised):
"Volumetric Image Segmentation on Multimodal Medical Images using Deep Learning";
Supervisor: E. Gröller;
Institute of Visual Computing & Human-Centered Technology,
final examination: 2020-07-25.
The automatic segmentation of tumors on different imaging modalities supports medical experts in patient diagnosis and treatment. Magnetic resonance imaging (MRl), 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 enabler 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 multimodal-specific tumor segmentations instead of single segmentations for a specific modality. This thesis aims to develop a segmentation method that uses multimodal context to improve t the modality-specific segmentation results. For the implementation, an artificial neural network is used, which is based on a fully convolution 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 MRl aid PET /CT scans of soft soft tissue tumors. The experiment investigated how different network architectures, multimodal fusion strategies, and input modalities affect the segmentation results. Tbc investigation showed that multimodal rondels lead to significantly better results than models for single modalities. Promising results have been achieved with multimodal models that segment several modality-specific tumor contours simultaneously.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.