@InProceedings{pmlr-v143-czolbe21a,
  title = 	 {Semantic similarity metrics for learned image registration},
  author =       {Czolbe, Steffen and Krause, Oswin and Feragen, Aasa},
  booktitle = 	 {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning},
  pages = 	 {105--118},
  year = 	 {2021},
  editor = 	 {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris},
  volume = 	 {143},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {07--09 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v143/czolbe21a/czolbe21a.pdf},
  url = 	 {https://proceedings.mlr.press/v143/czolbe21a.html},
  abstract = 	 {We propose a semantic similarity metric for image registration. Existing metrics like euclidean distance or normalized cross-correlation focus on aligning intensity values, giving difficulties with low intensity contrast or noise. Our approach learns dataset-specific features that drive the optimization of a learning-based registration model. We train both an unsupervised approach using an auto-encoder, and a semi-supervised approach using supplemental segmentation data to extract semantic features for image registration. Comparing to existing methods across multiple image modalities and applications, we achieve consistently high registration accuracy. A learned invariance to noise gives smoother transformations on low-quality images.}
}