V-Net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari F, Navab N, Ahmadi SA (2016)
Publication Type: Conference contribution
Publication year: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 565-571
Conference Proceedings Title: Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016
Event location: Stanford, CA, USA
ISBN: 9781509054077
DOI: 10.1109/3DV.2016.79
Abstract
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.
Involved external institutions
Germany (DE)How to cite
APA:
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016 (pp. 565-571). Stanford, CA, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-Net: Fully convolutional neural networks for volumetric medical image segmentation." Proceedings of the 4th International Conference on 3D Vision, 3DV 2016, Stanford, CA, USA Institute of Electrical and Electronics Engineers Inc., 2016. 565-571.
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