Abstract
In this paper, we present a novel and efficient method for brain tumor (and sub regions) segmentation in multimodal MR images based on a fully convolutional network (FCN) that enables end-to-end training and fast inference. Our structure consists of a downsampling path and three upsampling paths, which extract multi-level contextual information by concatenating hierarchical feature representation from each upsampling path. Meanwhile, we introduce a symmetry-driven FCN by the proposal of using symmetry difference images. The model was evaluated on Brain Tumor Image Segmentation Benchmark (BRATS) 2013 challenge dataset and achieved the state-of-the-art results while the computational cost is less than competitors.
Original language | English |
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Pages | 3864-3868 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 17 Sept 2017 |
Event | The International Conference on Image Processing 2017 - China National Convention Center, Beijing, China Duration: 17 Sept 2017 → 20 Dec 2017 |
Conference
Conference | The International Conference on Image Processing 2017 |
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Abbreviated title | IEEE ICIP 2017 |
Country/Territory | China |
City | Beijing |
Period | 17/09/17 → 20/12/17 |
Keywords
- FCN
- brain tumor segmentati