Segmentation of lung nodule in CT images based on mask R-CNN

Menglu Liu, Junyu Dong, Xinghui Dong, Hui Yu, Lin Qi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 languageEnglish
Title of host publication2018 9th International Conference on Awareness Science and Technology, iCAST 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)978-1-5386-5826-0
ISBN (Print)978-1-5386-5827-7
Publication statusPublished - 1 Nov 2018
Event9th IEEE International Conference on Awareness Science and Technology - Fukuoka, Japan
Duration: 19 Sept 201821 Sept 2018

Publication series

NameIEEE iCAST Proceedings Series
ISSN (Print)2325-5986
ISSN (Electronic)2325-5994


Conference9th IEEE International Conference on Awareness Science and Technology
Abbreviated titleiCAST 2018
Internet address


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