Medical Image Segmentation Techniques

Medical image segmentation is the computational task of partitioning scans—MRI, CT, ultrasound—into meaningful anatomical regions, such as isolating a tumor from surrounding tissue or delineating organ boundaries for surgical planning. Researchers draw on a range of mathematical frameworks, including graph cuts, level set methods, active contours, and statistical shape models, to automate or assist what clinicians have traditionally done by hand, a process that is both time-consuming and prone to inter-observer variability. Deep learning has recently reshaped the landscape, achieving strong performance on benchmark datasets, yet questions remain about how well these models generalize across scanners, imaging protocols, and rare pathologies where labeled training data is scarce. Ongoing work focuses on integrating prior anatomical knowledge with data-driven approaches and on developing registration methods that can align images taken at different times or from different modalities, enabling more reliable tracking of disease progression.

Works

86,193

Total citations

1,405,985

Keywords

Image SegmentationMedical Image AnalysisGraph CutsActive ContoursMRI SegmentationDeformable Image Registration