Name
Abstract | ||
|---|---|---|
The need for fast acquisition and automatic analysis of MRI data is growing in the age of big data. Although compressed sensing magnetic resonance imaging (CS-MRI) has been studied to accelerate MRI by reducing k-space measurements, in current CS-MRI techniques MRI applications such as segmentation are overlooked when doing image reconstruction. In this paper, we test the utility of CS-MRI methods in automatic segmentation models and propose a unified deep neural network architecture called SegNetMRI which we apply to the combined CS-MRI reconstruction and segmentation problem. SegNetMRI is built upon a MRI reconstruction network with multiple cascaded blocks each containing an encoder-decoder unit and a data fidelity unit, and MRI segmentation networks having the same encoder-decoder structure. The two subnetworks are pre-trained and fine-tuned with shared reconstruction encoders. The outputs are merged into the final segmentation. Our experiments show that SegNetMRI can improve both the reconstruction and segmentation performance when using compressive measurements. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Iterative reconstruction,Fidelity,Pattern recognition,Segmentation,Computer science,Neural network architecture,Encoder,Artificial intelligence,Big data,Compressed sensing,Magnetic resonance imaging |
DocType | Volume | Citations |
Journal | abs/1805.02165 | 1 |
PageRank | References | Authors |
0.35 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyan Sun | 1 | 7 | 3.16 |
| Zhiwen Fan | 2 | 29 | 3.15 |
| Yue Huang | 3 | 317 | 29.82 |
| Xinghao Ding | 4 | 591 | 52.95 |
| John Paisley | 5 | 1003 | 55.70 |