Deep Learning-Based Segmentation of MRI Images: Concepts, Challenges, Deep Learning Architectures, and Future Directions

Medical image segmentation plays a crucial role in the accurate diagnosis and treatment of various diseases, especially in the detection and diagnosis of brain tumors. Image segmentation is a critical task in MRI analysis, as it enables the identification and characterization of anatomical structures in the images. In recent years, numerous techniques have been proposed for brain tumor segmentation, ranging from traditional methods to deep learning-based approaches. With recent advancements, deep learning-based methods have shown promising results in brain tumor segmentation, with convolutional neural networks (CNNs) being the most commonly used approach in the medical image analysis community. In this paper, we provide a comprehensive study of deep learning-based segmentation of MRI images, covering the fundamental concepts, challenges, deep neural network architectures, and future directions.