Statistical Shape

Statistical shape modeling (SSM) aims to quantitatively analyze and represent the variations in anatomical shapes within a population, facilitating applications in medical diagnosis and treatment planning. Current research heavily emphasizes deep learning approaches, including various neural network architectures, to directly learn SSMs from unsegmented medical images or point clouds, thereby reducing the need for time-consuming manual segmentation. This shift towards automated SSM construction is significantly improving the feasibility and accessibility of shape analysis, impacting fields like medical image analysis, computer-aided surgery, and personalized medicine.