Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

@article{Oktay2017AnatomicallyCN,
  title={Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation},
  author={Ozan Oktay and Enzo Ferrante and Konstantinos Kamnitsas and Mattias P. Heinrich and Wenjia Bai and Jose Caballero and Stuart A. Cook and Antonio de Marvao and Timothy J. W. Dawes and Declan P. O’Regan and Bernhard Kainz and Ben Glocker and Daniel Rueckert},
  journal={IEEE Transactions on Medical Imaging},
  year={2017},
  volume={37},
  pages={384-395}
}
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. [] Key Method State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end.

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