An Overview Of Segmentation Techniques For CT And MRI Images:

Description

Advancements in medical image segmentation are transforming clinical diagnostics and treatment planning. This review examines a variety of segmentation techniques applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), highlighting their clinical applications and future potential. CT segmentation, using methods like U-Net and nnU-Net, enables precise tumor delineation in oncology, coronary artery analysis in cardiology, and lung lesion detection in pulmonology, enhancing radiotherapy targeting, surgical planning, and diagnostic precision. MRI segmentation benefits from superior soft tissue contrast, with techniques such as Mask R-CNN excelling in identifying brain lesions, monitoring soft tissue tumors, and guiding interventions. U-Net and Attention U-Net architectures have proven highly effective in brain, prostate, and musculoskeletal imaging. A systematic review contrasts traditional methods, like thresholding, with advanced deep learning approaches, emphasizing strengths, limitations, and performance metrics. Future directions include 3D/4D segmentation, multimodal data fusion, and enhancing AI explainability.