This course has been designed as a topic-driven learning course. The focus of this semester is on 2D and 3D segmentation techniques for various medical applications. Segmentation is an essential process for most quantitative medical image analyses, e.g., tumor characterization, brain function analysis, vessel analysis, etc. The classes will be divided into two parts. In the first part, the instructor will present background knowledge for such widely-used techniques as deformable models, level set methods, watershed transformation based approaches, wavelet transformation based approaches, morphological operators, and so on. In the second part, each student will be required to present a paper on image segmentation. Furthermore, the students will be teamed up into two or three groups, each of which needs to solve a practical medical problem by implementing the state-of-the-art segmentation techniques.