Medical Image Segmentation based on Deep Learning: A Review

This study focuses on utilizing deep learning techniques for segmenting medical images, such as MRI and CT scans. The paper explores the limitations of traditional segmentation methods and highlights the potential of deep learning in overcoming these challenges. It provides an overview of Convolutional Neural Networks (CNNs) and their adaptation for medical image segmentation. Various architectures like U-Net, FCNs, and DeepLab are discussed, along with the importance of data augmentation and handling class imbalance. The paper also covers training processes, post-processing techniques, and evaluation metrics. It concludes by discussing current trends, challenges, and future directions in the field.