Deep Learning Approaches for Real-Time Medical Image Segmentation

Abstract

Medical image segmentation is an important feature of clinical diagnostics, surgical planning, and disease monitoring as it provides an opportunity to segment anatomy and pathological regions with high precision. By imaging modalities, tissue contrast, and noise, conventional image processing and machine learning approaches are known to have issues of image variability, although they are effective in specific situations. In the previous years, deep learning (DL) and convolutional neural networks (CNNs) specifically and its offshoots have revolutionized the medical image analysis by providing cutting-edge precision in segmentation across a great variety of modalities such as MRI, CT, PET, and ultrasound. The paper reviews development of the deep learning architecture, infrastructure, and implementation of deep learning models in real-time segmentation of medical images based on their performance in computation, generalization, and clinical utility. The architectures that are discussed in detail include U-Net, SegNet, DeepLabV3+, Attention U-Net, and Transformer-based (Swin-Unet, TransUNet) and their advantages and disadvantages. The model pruning, quantization and GPU acceleration are some of the optimization methods that the study has taken into the consideration to enhance the real-time performance. These problems as data scarcity, class imbalance, explainability, and new trends of federated learning and use of edge AI in medical imaging are also addressed. The findings indicate that real time high-precise segmentation currently becomes a reality with the integration of deep learning and highperformance computing systems and cloud based systems and has preconditioned the intelligent and automated clinical decision support systems.