Deep learning for automatic segmentation of tumors on MRI

Abstract

A crucial step of the radiotherapy workflow is the segmentation of the tumors. Currently, this is done manually, which is very time-consuming and therefore puts a lot of burden in the clinical workflow. Deep learning techniques, currently state of the art for computer vision tasks, are a potential solution to speed up the process. Even though they have been applied to segment other structures of interest for the RT workflow, they still underperform for the case of tumors. Furthermore, there is limited research in automatic segmentation of tumors for the particular case of MRI, even though this imaging modality presents better soft tissue contrast and it is therefore ideal to visualize tumors. The goal of this thesis was to develop automatic segmentation techniques for tumors on MRI images that deliver clinically acceptable segmentations. The different automatic segmentation methods were applied in two different tasks: the automatic segmentation of the oropharyngeal primary tumor in multiparametric diagnostic MRI images and the automatic segmentation of the cervical cancer gross tumor volume in the MRI images of the brachytherapy treatment images.