Atrous Convolution for Binary Semantic Segmentation of Lung Nodule

Publication Type:
Conference Proceeding
Citation:
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019, 2019-May pp. 1015 - 1019
Issue Date:
2019-05-01
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20190208_ICASSP2019_CRV_hesam.pdfAccepted Manuscript version538.51 kB
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© 2019 IEEE. Accurately estimating the size of tumours and reproducing their boundaries from lung CT images provides crucial information for early diagnosis, staging and evaluating patients response to cancer therapy. This paper presents an advanced solution to segment lung nodules from CT images by employing a deep residual network structure with Atrous convolution. The Atrous convolution increases the field of view of the filters and helps to improve classification accuracy. Moreover, in order to address the significant class imbalance issue between the nodule pixels and background non-nodule pixels, a weighted loss function is proposed. We evaluate our proposed solution on the widely adopted benchmark dataset LIDC. A promising result of an average DCS of 81.24% is achieved, outperforming the state of the arts. This demonstrates the effectiveness and importance of applying the Atrous convolution and weighted loss for such problems.
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