A Statistical Shape Model (SSM) is a mathematical framework used to analyze and represent the shapes of objects in a statistical manner. It captures the variations in shape by using a set of training examples, allowing for the creation of a model that can describe typical shapes and their deviations. This is particularly useful in fields like computer vision and medical imaging, where understanding the shape of anatomical structures is crucial. The SSM is built by applying techniques such as Principal Component Analysis (PCA) to a collection of shapes, enabling the identification of key features and variations. Once established, the model can be used for tasks like shape recognition, segmentation, and reconstruction, making it a valuable tool in applications involving biomedical research and image analysis.