ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging
Jiancheng Yang, Udaranga Wickramasinghe, Bingbing Ni, Pascal Fua
Abstract
Deep implicit shape models have become popular in the computer vision community at large but less so for biomed-ical applications. This is in part because large training databases do not exist and in part because biomedical an-notations 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 ca-pacity at negligible computational cost. Extensive experi-ments on three medical shape datasets prove the superiority over current implicit representation methods.