ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging
Jiancheng Yang, Udaranga Wickramasinghe, Bingbing Ni, Pascal Fua
Deep implicit shape models have become popular in the computer vision community at large but less so for biomedical applications. This is in part because large training databases do not exist and in part because biomedical annotations are often noisy. In this paper, we show that by introducing templates within the deep learning pipeline we can overcome these problems. The proposed framework, named ImplicitAtlas, represents a shape as a deformation field from a learned template field, where multiple templates could be integrated to improve the shape representation capacity at negligible computational cost. Extensive experiments on three medical shape datasets prove the superiority over current implicit representation methods.