An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images
Research output: Contribution to conference › Paper › Research › peer-review
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An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images. / Chen, Shuai; Bruijne, Marleen de.
2018.Research output: Contribution to conference › Paper › Research › peer-review
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TY - CONF
T1 - An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images
AU - Chen, Shuai
AU - Bruijne, Marleen de
PY - 2018/11/8
Y1 - 2018/11/8
N2 - Fully-connected Conditional Random Field (CRF) is often used as post-processing to refine voxel classification results by encouraging spatial coherence. In this paper, we propose a new end-to-end training method called Posterior-CRF. In contrast with previous approaches which use the original image intensity in the CRF, our approach applies 3D, fully connected CRF to the posterior probabilities from a CNN and optimizes both CNN and CRF together. The experiments on white matter hyperintensities segmentation demonstrate that our method outperforms CNN, post-processing CRF and different end-to-end training CRF approaches.
AB - Fully-connected Conditional Random Field (CRF) is often used as post-processing to refine voxel classification results by encouraging spatial coherence. In this paper, we propose a new end-to-end training method called Posterior-CRF. In contrast with previous approaches which use the original image intensity in the CRF, our approach applies 3D, fully connected CRF to the posterior probabilities from a CNN and optimizes both CNN and CRF together. The experiments on white matter hyperintensities segmentation demonstrate that our method outperforms CNN, post-processing CRF and different end-to-end training CRF approaches.
KW - cs.CV
UR - https://sites.google.com/view/med-nips-2018/abstracts
M3 - Paper
ER -
ID: 216262545