Name
Abstract | ||
|---|---|---|
Due to the low-quality of CT images, the lack of annotated data, and the complex shapes of lung nodules, existing methods for lung nodules detection only predict the center of the nodule, whereas the nodule size is a very important diagnostic criteria but is neglected. In this paper, we employed the powerful object detection neural network “Mask R-CNN” for lung nodule segmentation, which provides contour information. Because of the imbalance between positive and negative samples, we trained classification networks based on block. We selected the classification network with the hightest accuracy. The selected classification network was used as the backbone of the image segmentation network-Mask R-CNN, which performs excellently on natural images. Lastly, Mask R-CNN model trained on the COCO data set was fine-tuned to segment pulmonary nodules. The model was tested on the LIDC-IDRI dataset. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICAwST.2018.8517248 | 2018 9th International Conference on Awareness Science and Technology (iCAST) |
Keywords | Field | DocType |
deep learning,lung nodule segmentation,Mask R-CNN,LIDC-IDRI | Object detection,Pattern recognition,Segmentation,Computer science,Image segmentation,Artificial intelligence,Deep learning,Artificial neural network | Conference |
ISSN | ISBN | Citations |
2325-5986 | 978-1-5386-5827-7 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Menglu Liu | 1 | 1 | 0.35 |
| Junyu Dong | 2 | 393 | 77.68 |
| Xinghui Dong | 3 | 14 | 5.00 |
| Hui Yu | 4 | 128 | 21.50 |
| Lin Qi | 5 | 18 | 6.47 |