Deep Learning-Driven Knowledge Transfer for Automated Medical Image Segmentation

This study presents a comprehensive review and comparative analysis of existing research on image segmentation techniques for MRI imaging. A detailed comparison is made between single-image and multispectral image segmentation, as well as between supervised and unsupervised segmentation approaches. Additional aspects such as image pre-processing, image registration, validation methods, and their classifications are also discussed. In medical image processing, segmentation is an essential step, especially for applications like brain MRI analysis, where segmentation results help in understanding brain anatomy, identifying structural changes, and aiding in clinical tasks such as surgical planning or image-guided treatments. Automated Knowledge Transfer (AKT) using deep learning, particularly through pre-trained convolutional neural networks enables efficient learning from large datasets, significantly reducing manual labeling efforts and enabling faster, more accurate image analysis. This study also discusses challenges and solutions within MRI segmentation, highlighting a case study on tumor volume measurement to illustrate practical applications.