Name
Title | ||
|---|---|---|
Deep contextual residual network for electron microscopy image segmentation in connectomics |
Abstract | ||
|---|---|---|
The goal of connectomics research is to manifest the mechanisms and functions of neural system by using electron microscopy (EM). One of the biggest challenges in connectomic reconstruction is developing reliable neuronal membranes segmentation method to reduce the burden on manual neurite labeling and validation. In this paper, we put forward an effective deep learning approach to realize neuronal membranes segmentation in EM image stacks, which utilizes spatially efficient residual network and multilevel representations of contextual cues to achieve accurate segmentation performance. Furthermore, multicut is used as post-processing to optimize the outputs of network. Experimental results on the public dataset of ISBI 2012 EM Segmentation Challenge demonstrate the effectiveness of our approach in neuronal membranes segmentation. Our method now ranks top 3 among 88 teams and yields 0.98356 Rand Score as well as 0.99063 Information Score, which outperforms most of state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISBI.2018.8363597 | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
Keywords | Field | DocType |
Connectomics,Deep Learning,Image Segmentation,Electron Microscopy | Residual,Computer vision,Connectomics,Pattern recognition,Segmentation,Computer science,Image segmentation,Neural system,Artificial intelligence,Deep learning | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-5386-3637-4 | 2 |
PageRank | References | Authors |
0.41 | 0 | 6 |