Please use this identifier to cite or link to this item: https://doi.org/10.1109/TMI.2017.2743464
Title: Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
Authors: Oktay O.
Ferrante E.
Kamnitsas K.
Heinrich M.
Bai W.
Caballero J.
Cook S.A. 
De Marvao A.
Dawes T.
O'Regan D.P.
Kainz B.
Glocker B.
Rueckert D.
Keywords: convolutional neural network
image super-resolution
medical image segmentation
Shape prior
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Oktay O., Ferrante E., Kamnitsas K., Heinrich M., Bai W., Caballero J., Cook S.A., De Marvao A., Dawes T., O'Regan D.P., Kainz B., Glocker B., Rueckert D. (2018). Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. IEEE Transactions on Medical Imaging 37 (2) : 384-395. ScholarBank@NUS Repository. https://doi.org/10.1109/TMI.2017.2743464
Abstract: Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge. 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. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks. In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies. © 1982-2012 IEEE.
Source Title: IEEE Transactions on Medical Imaging
URI: http://scholarbank.nus.edu.sg/handle/10635/150626
ISSN: 02780062
DOI: 10.1109/TMI.2017.2743464
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