Segmentation is the task of extracting regions of interest from images. Given a medical context like in our group, this usually means that in images from medical modalities (e.g. magnetic resonance imaging, computed tomography, or ultrasound), organs have to be delineated for quantitative measurements or shape analysis. Likewise, manifestations of disease, such as tumors, need to be traced for tracking disease status and assessing treatment effects. Image segmentation has always played a major role in research at CIAN. In earlier projects, motion segmentation was used as part of tracking the movement of organs induced by breathing, and facial muscles were segmented for maxillofacial surgery planning. Current projects include gray matter–white matter segmentation in the spinal cord for multiple sclerosis research, detecting and delineating multiple sclerosis lesions in the brain, developing an augmented reality device for lymph node identification in cancer patients, and reconstructing the cornea surface for eye surgery planning. The approaches to solve our tasks are as various as the tasks themselves. Methods employed in our group reach from classical ones like variational segmentation to the more recent deep learning techniques.