TL
TL;DR
This work proposes the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation, and shows that the proposed models achieve top performances with fewer parameters and faster computation.
Abstract
Deep learning has shown its great promise in various biomedical image segmentation tasks. Existing models are typically based on U-Net and rely on an encoder-decoder architecture with stacked local operators to aggregate long-range information gradually. However, only using the local operators limits the efficiency and effectiveness. In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation. These blocks can be inserted into U-Net as size-preserving processes, as well as down-sampling and up-sampling layers. We perform thorough experiments on the 3D multimodality isointense infant brain MR image segmentation task to evaluate the non-local U-Nets. Results show that our proposed models achieve top performances with fewer parameters and faster computation.
References40 items
1
Learning Hierarchical and Shared Features for Improving 3D Neuron Reconstruction
2
3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation
3
Computational modeling of cellular structures using conditional deep generative networks
4
Non-local Neural Networks
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