A Systematic Review of Deep Neural Network Architectures Training Methods and Applications for Image Segmentation

Image segmentation, a critical task in computer vision, has witnessed transformative advancements driven by deep neural networks (DNNs), enabling unprecedented precision across diverse applications, including medical imaging, industrial automation, and autonomous navigation. This systematic review elucidates the evolving architecture of segmentations with a focus on state-of-the-art developments from the year 2020 onwards. The classical architectures, such as U-Net, Fully Convolutional Networks, and DeepLab, are discussed in terms of their foundational methodologies, while the very recent Innovations-Vision Transformers, Swin Transformers, and SegFormer-are underlined for their long-range dependency modelling capability and efficient capture of hierarchical multi-scale features. Advanced training paradigms are explained, including transfer learning, data augmentation with Albumentations, and modern optimization algorithms such as AdamP, in order to demonstrate the degree to which these concepts further enhance model performance, robustness, and generalizability. Real-world implementations critically discuss medical applications, such as tumor segmentation and robotic surgery guidance, and industrial applications, such as defect detection and object tracking in dynamic environments. It addresses the rising complexity of deployment by focusing on key challenges: high computation demands, real-time inference constraints, and domain generalization, among others, discussing mitigation strategies such as model quantization, domain adaptation, and hybrid architectures. This review integrates the theoretical development with insights into practice through comparative analyses, performance benchmarking, and code implementation, thereby serving as a guide for designing and deploying advanced segmentation systems. The contribution bridges the gap between the innovation-application cycle in laying the foundation for future research towards the optimization of the segmentation model for accuracy, efficiency, and scalability.