Region proposal network for lung nodule detection and segmentation
- Publication Type:
- Conference Proceeding
- Citation:
- CEUR Workshop Proceedings, 2020, 2675, pp. 48-52
- Issue Date:
- 2020-01-01
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© 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Lung nodule detection and segmentation play a critical role in detecting and determining the stage of lung cancer. This paper proposes a two-stage segmentation method which is capable of improving the accuracy of detecting and segmentation of lung nodules from 2D CT images. The first stage of our approach proposes multiple regions, potentially containing the tumour, and the second stage performs the pixel-level segmentation from the resultant regions. Moreover, we propose an adaptive weighting loss to effectively address the issue of class imbalance in lung CT image segmentation. We evaluate our proposed solution on a widely adopted benchmark dataset of LIDC. We have achieved a promising result of 92.78% for average DCS that puts our method among the top lung nodule segmentation methods.
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