| Περίληψη: | Volumetric segmentation in magnetic resonance images is mandatory for the diagnosis,
monitoring, and treatment planning. Manual practices require anatomical knowledge, are
expensive, time consuming and can be inaccurate due to human factor. Automated
segmentation can save physicians time and provide an accurate reproducible solution for
further analysis. In this thesis, automated brain segmentation from multi-modal 3D magnetic
resonance images (MRIs) is studied. An extensive comparative analysis of state-of-the-art 3D
deep neural networks for brain sub-region segmentation is performed. We start by describing
the fundamentals of MR Imaging because it is crucial to understand your input data to train a
deep architecture. Then, we provide the reader with an overview of how deep learning works
by extensively analyzing every component (layer) of a deep network. After we study the fields
of magnetic resonance and deep learning separately, we attempt give a broader perspective
of the intersection of this two fields with a different range of application of deep networks,
from MR image reconstruction to medical image generation.
Our work is focused on multi-modal brain segmentation. For our experiments, we used two
common benchmark datasets from medical image challenges. Brain MR segmentation
challenges aim to evaluate state-of-the-art methods for the segmentation of brain by
providing a 3D MRI dataset with ground truth tumor segmentation labels annotated by
physicians. In order to evaluate state-of-the-art 3D architectures, we briefly analyze the
author’s approaches, as well as to provide the reader with an intuition behind the design
choices. We perform a comparative analysis of the baseline architectures through extensive
evaluations. The implemented networks were based on the specifications of the original
papers. Finally, we discuss the reported results and provide future directions for implementing
an open-source medical segmentation library in PyTorch along with data loaders of the most
common medical MRI datasets. The goal is to produce a 3D deep learning library for medical
imaging related tasks. We strongly believe in open and reproducible deep learning research.
In order to reproduce our results, the code (alpha release) and materials of this thesis are
available in https://github.com/black0017/MedicalZooPytorch
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