| Autor: |
Lee MCH, Petersen K, Pawlowski N, Glocker B, Schaap M |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2019 Nov; Vol. 38 (11), pp. 2596-2606. Date of Electronic Publication: 2019 Mar 22. |
| DOI: |
10.1109/TMI.2019.2905990 |
| Abstrakt: |
In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors, and this is free of discretization artifacts by providing a soft partial volume segmentation. We also introduce a simple yet effective way of incorporating priors in the state-of-the-art pixel-wise binary classification methods such as fully convolutional networks and U-net. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. We report results on synthetic data and sub-voxel segmentation of coronary lumen structures in cardiac computed tomography showing the benefit of incorporating priors in neural network-based image segmentation. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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