ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging

Jiancheng Yang, Udaranga Wickramasinghe, Bingbing Ni, Pascal Fua

CVPR 2022 pp. 15861-15871

doi:10.1109/CVPR52688.2022.01540 /cvpr/2022/yang2022cvpr-implicitatlas/

Abstract

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.

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Cite

Text

Yang et al. "ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.01540

Markdown

[Yang et al. "ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/yang2022cvpr-implicitatlas/) doi:10.1109/CVPR52688.2022.01540

BibTeX

@inproceedings{yang2022cvpr-implicitatlas,
  title     = {{ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging}},
  author    = {Yang, Jiancheng and Wickramasinghe, Udaranga and Ni, Bingbing and Fua, Pascal},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2022},
  pages     = {15861-15871},
  doi       = {10.1109/CVPR52688.2022.01540},
  url       = {https://mlanthology.org/cvpr/2022/yang2022cvpr-implicitatlas/}
}