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) |
| Publisher | IEEE |
| 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/ |
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
Rights statement: © © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript (Post-print), 1000 KB, PDF-document
Related information
ID: 11202265
