- Title
- Adjacent network for semantic segmentation of liver CT scans
- Creator
- Astono, Indriani; Welsh, James S.; Chalup, Stephan
- Relation
- IEEE BIBE 2018: The 18th IEEE International Conference on Bioinformatics and Bioengineering. Proceedings of the 18th IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2018) (Taichung, Taiwan 29-31 October, 2018) p. 35-40
- Publisher Link
- http://dx.doi.org/10.1109/BIBE.2018.00015
- Publisher
- IEEE Computer Society
- Resource Type
- conference paper
- Date
- 2018
- Description
- Fully convolutional neural networks have shown remarkable success in performing semantic segmentation. The use of convolutional layers for the entire architecture and skip connections to combine different resolution features or predictions have been adopted in successful networks, such as U-Net and DenseNet. However, these models employ several max-pooling layers that cause the network to lose spatial information and require them to mimic an autoencoder architecture to perform semantic segmentation at the original input resolution. In this paper, we propose a network that extracts features automatically with convolutional layers, like the fully convolutional neural network, but retains the spatial information of each of the extracted features. It then utilises the extracted features to make predictions with an efficient upsampling method. We evaluate the network performance on a liver segmentation task where it performs with comparable accuracy to other state-of-the-art networks while being much smaller in terms of the number of parameters as well as faster in computation time.
- Subject
- convolutional neural networks; spatial information; medical image application; CNNs
- Identifier
- http://hdl.handle.net/1959.13/1405948
- Identifier
- uon:35565
- Rights
- © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- Language
- eng
- Full Text
- Reviewed
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