Name
Abstract | ||
|---|---|---|
Neuron tracing, also known as neuron reconstruction, studies 3D morphologies of neurons based on imaging data. Neuron reconstruction is of fundamental importance in computational neuroscience since it is a crucial step towards reverse engineering of the wiring and functions of a brain. On the other hand, it is not possible to manually trace all neurons due to the complexity and cost of this task. Hence, it raises the need of building a computational pipeline to perform automatic neuron reconstruction. In this work, we propose a deep learning approach for improving the accuracy of 3D neuron reconstruction. First, we propose to learn shared features among different images in the whole dataset. Such shared features are learned automatically by our model at different scales. Second, we propose to incorporate such features to guide the information flow in the network. Specifically, we propose to build skip connections between the encoder and the decoder of our networks by incorporating the hierarchical and shared features. Our proposed skip connections are built based on the attention mechanism, where the hierarchical shared features serve as the query matrix and the local input features serve as the key and value matrices. Since the parameters are learned automatically, we expect that only useful spatial information is transmitted to the decoder. We conduct both qualitative and quantitative experiments to demonstrate the effectiveness of our proposed method. Experimental results show that our proposed model has the ability to capture detailed structural information for neurons. Our results also demonstrate that the proposed model is robust to noise. In addition, quantitative evaluations show that our method achieves better performance than other approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDM.2019.00091 | 2019 IEEE International Conference on Data Mining (ICDM) |
Keywords | Field | DocType |
Deep Learning, 3D Neuron Reconstruction, Image Segmentation | Spatial analysis,Data mining,Information flow (information theory),Computational neuroscience,Computer science,Reverse engineering,Image segmentation,Encoder,Artificial intelligence,Deep learning,Machine learning,Tracing | Conference |
ISSN | ISBN | Citations |
1550-4786 | 978-1-7281-4605-8 | 1 |
PageRank | References | Authors |
0.37 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Yuan | 1 | 27 | 6.64 |
| Na Zou | 2 | 10 | 2.67 |
| shaoting zhang | 3 | 1831 | 92.08 |
| Hanchuan Peng | 4 | 3930 | 182.27 |
| Shuiwang Ji | 5 | 2579 | 122.25 |