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Deep learning for segmentation of brain tumors and organs at risk in radiotherapy planning

Abstract : Medical images play an important role in cancer diagnosis and treatment. Oncologists analyze images to determine the different characteristics of the cancer, to plan the therapy and to observe the evolution of the disease. The objective of this thesis is to propose efficient methods for automatic segmentation of brain tumors and organs at risk in the context of radiotherapy planning, using Magnetic Resonance (MR) images. First, we focus on segmentation of brain tumors using Convolutional Neural Networks (CNN) trained on MRIs manually segmented by experts. We propose a segmentation model having a large 3D receptive field while being efficient in terms of computational complexity, based on combination of 2D and 3D CNNs. We also address problems related to the joint use of several MRI sequences (T1, T2, FLAIR). Second, we introduce a segmentation model which is trained using weakly-annotated images in addition to fully-annotated images (with voxelwise labels), which are usually available in very limited quantities due to their cost. We show that this mixed level of supervision considerably improves the segmentation accuracy when the number of fully-annotated images is limited.\\ Finally, we propose a methodology for an anatomy-consistent segmentation of organs at risk in the context of radiotherapy of brain tumors. The segmentations produced by our system on a set of MRIs acquired in the Centre Antoine Lacassagne (Nice, France) are evaluated by an experienced radiotherapist.
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Pawel Mlynarski. Deep learning for segmentation of brain tumors and organs at risk in radiotherapy planning. Artificial Intelligence [cs.AI]. COMUE Université Côte d'Azur (2015 - 2019), 2019. English. ⟨NNT : 2019AZUR4084⟩. ⟨tel-02358374v2⟩

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