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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the 9th IEEE International Conference on Awareness Science and Technology
Subtitle of host publicationiCAST 2018
Publication statusAccepted for publication - 24 Jul 2018
Event9th IEEE International Conference on Awareness Science and Technology - Fukuoka, Japan
Duration: 19 Sep 201821 Sep 2018


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