Segmentation of lung nodule in CT images based on mask R-CNN
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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.
| Original language | English |
|---|---|
| Title of host publication | 2018 9th International Conference on Awareness Science and Technology, iCAST 2018 |
| Publisher | IEEE |
| Pages | 95-100 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5386-5826-0 |
| ISBN (Print) | 978-1-5386-5827-7 |
| DOIs | |
| Publication status | Published - 1 Nov 2018 |
| Event | 9th IEEE International Conference on Awareness Science and Technology - Fukuoka, Japan Duration: 19 Sep 2018 → 21 Sep 2018 http://www.design.kyushu-u.ac.jp/~icast/ |
Publication series
| Name | IEEE iCAST Proceedings Series |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 2325-5986 |
| ISSN (Electronic) | 2325-5994 |
Conference
| Conference | 9th IEEE International Conference on Awareness Science and Technology |
|---|---|
| Abbreviated title | iCAST 2018 |
| Country | Japan |
| City | Fukuoka |
| Period | 19/09/18 → 21/09/18 |
| Internet address |
Documents
- Segmentation of Lung Nodule in CT Images_postprint
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Accepted author manuscript (Post-print), 0.98 MB, PDF document
Related information
ID: 11202265
