Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation

Understand

Shape information is a strong and valuable prior in segmenting organs in medical images.

• However, most current deep learning based segmentation algorithms have not taken shape information into consideration, which can lead to bias towards texture.
• We aim at modeling shape explicitly and using it to help medical image segmentation.
• Previous methods proposed Variational Autoencoder (VAE) based models to learn the distribution of shape for a particular organ and used it to automatically evaluate the quality of a segmentation prediction by fitting it into the learned shape distribution.