Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more.
The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation.
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Table of Contents
1. Introduction to brain tumor segmentation using Deep Learning 2. Data preprocessing methods needed in brain tumor segmentation 3. Transformation of low-resolution brain tumor images into super-resolution images using Deep Learning based methods 4. Single path Convolutional Neural Network based brain tumor segmentation 5. Multi path Convolutional Neural Network based brain tumor segmentation 6. Fully Convolutional Networks (FCNs) based brain tumor segmentation 7. Cascade convolutional neural network-based brain tumor segmentation 8. Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) for brain tumor segmentation 9. Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN) for brain tumor segmentation 10. Generative Adversarial Networks (GAN) based brain tumor segmentation 11. Auto encoder-based brain tumor segmentation 12. Ensemble deep learning model-based brain tumor segmentation 13. Research Issues and Future of Deep Learning based brain tumor segmentation
