An overview of segmentation techniques for CT and MRI images: Clinical implications and future directions in medical diagnostics

Advancements in Medical Image Segmentation have revolutionized clinical diagnostics and treatment planning. This review explores a wide range of segmentation techniques applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images, emphasizing their clinical implications and future directions. CT segmentation techniques, including U-Net and its variant nnU-Net, are essential in oncology for precise tumor delineation, in cardiology for coronary artery analysis, and in pulmonology for lung lesion detection. These methods enhance radiotherapy targeting, surgical planning, and overall diagnostic accuracy. The nnU-Net, known for its self-configuring nature, is particularly notable for setting new benchmarks in medical image segmentation tasks. MRI segmentation benefits from superior soft tissue contrast. Techniques like Mask Region-based Convolutional Neural Network (R-CNN) excel in identifying brain lesions, assessing musculoskeletal injuries, and monitoring soft tissue tumors. These methods support detailed visualization of internal structures, improving diagnosis and guiding targeted interventions. U-Net architectures also play a critical role in MRI segmentation, demonstrating high efficacy in various applications such as brain and prostate imaging. A systematic review of the literature reveals performance metrics for various segmentation techniques, such as accuracy, sensitivity, specificity, and processing time. Traditional methods like thresholding and edge detection are contrasted with advanced deep learning and machine learning approaches, highlighting the strengths and limitations of each. The review also addresses methodological approaches, including data collection, analysis, and evaluation metrics. Future prospects include integrating 3D and 4D segmentation, multimodal data fusion, and enhancing AI explain ability. These innovations aim to refine diagnostic processes, personalize treatments, and improve patient outcomes. Clinical applications of these segmentation techniques demonstrate significant advantages in radiology, oncology, and cardiology, though challenges such as data variability and noise persist. Emerging strategies like data augmentation and transfer learning offer potential solutions to these issues. The continuous evolution of medical image segmentation techniques promises to enhance diagnostic accuracy, efficiency, and the personalization of patient care, ultimately leading to better healthcare outcomes.