Deep Learning Approaches for Medical Images Segmentation: A Systematic Review
Keywords:
Medical Image Segmentation, Medical Image Analysis, Computer-Aided Diagnosis and Treatment, Deep Learning Architectures, Uncertainty Quantification, Evaluation Metrics, State-Of-The-Art Architecture, Systematic ReviewAbstract
Medical image segmentation is a critical component of computer-aided diagnosis and treatment planning, with advancements driven by continuous research in deep learning architectures. This systematic review explores state-of-the-art models for medical image segmentation, focusing on recent developments and innovations. The review covers prominent architectures, such as UNet++, nnU-Net, HRNet, Vision Transformer (ViT), and DUCK-Net, each contributing to improved segmentation accuracy and efficiency. Additionally, it discusses traditional and deep learning-based approaches, highlighting the effectiveness of convolutional neural networks (CNNs) and fully convolutional networks (FCNs). The integration of uncertainty quantification methodologies, particularly Bayesian neural networks (BNNs), is examined to enhance interpretability and reliability in medical imaging models. Evaluation metrics, including F- measure-based metrics, sensitivity, specificity, and the impact of class imbalance, are thoroughly analyzed to ensure robust algorithmic assessment. The review emphasizes the significance of metrics like the Dice Similarity Coefficient (DSC) and Intersection-over-Union (IoU) in addressing challenges posed by class imbalance in medical image segmentation. The comprehensive synthesis aims to provide a detailed overview of the current landscape, aiding researchers and practitioners in navigating the evolving field of medical image segmentation. The systematic search and selection methodology ensures a rigorous examination of relevant literature, contributing to a comprehensive understanding of the advancements in this critical domain.
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