Automatic Brain Tumor Segmentation with a 3-Dimensional Generative Adversarial Neural Network
| Autor: | Mpendulo Mamba |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Brain tumor segmentation is a very crucial task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amounts of magnetic resonance images (MRI) generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain image segmentation. In this work, we demonstrate a deep neural network for volumetric segmentation that learns from a series of annotated volumetric images given in the Neuroimaging Informatics Technology Initiative (NIfTI) format. Recently, automatic segmentation using deep learning methods proved effective since these methods achieve state-of-the-art results and can address the problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based images. We investigate 3D conditional adversarial networks as a novel solution to 3D image segmentation for medical segmentation problems. These networks not only learn the mapping from input images to output images, but also learn a loss function to train the mapping between them. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We show that this method is effective at generating slices of segmentation data from 3D labelled maps. We utilize a dataset from the medical image computing and computer assisted intervention (MICCAI), which consists of MRI scans of high-grade gliomas (HGG) which are tumors of the central nervous system and low-grade gliomas (LGG) which are referred to as slow-growing tumors. The proposed model is able to discriminate between well segmented and poorly segmented images and the generative model can create segmentation image masks around the tumors and achieves an 80.57% dice score when compared with the dataset. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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