Lung fields segmentation in chest radiographs using Dense-U-Net and fully connected CRF

Yuqin Li; Xiao Dong; Weili Shi; Yu Miao; Huamin Yang; Zhengang Jiang

doi:10.1117/12.2589384

27 January 2021 Lung fields segmentation in chest radiographs using Dense-U-Net and fully connected CRF

Yuqin Li, Xiao Dong, Weili Shi, Yu Miao, Huamin Yang, Zhengang Jiang

Author Affiliations +

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172011 (2021) https://doi.org/10.1117/12.2589384
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Computer-Aided Diagnosis (CAD) benefits from its early diagnosis and accurate treatment of lung diseases. Accurate segmentation of lung fields is an important component in CAD for lung health, which facilitates subsequent analysis. However, most of the existing algorithms for lung fields segmentation are unable to ensure appearance and spatial consistency due to the varied boundaries and poor contrasts. In this paper, we propose a novel and hybrid method for lung fields segmentation by integrating Dense-U-Net network and a fully connected conditional random field (CRF). In order to realize the reuse of image features, the structure of densely-connected is added to the decoder, which ensures the object with varied shapes and sizes can be extracted without adding more parameters. To make full use of the mutual information among pixels of the original image, a fully connected CRF algorithm is adopted to further optimize the preliminary segmentation results according to the intensity and position of each pixel. Compared with some previous popular methods on JSRT dataset, the proposed method in this paper shows higher Jaccard index and Dice-Coefficient.

Citation Download Citation

Yuqin Li, Xiao Dong, Weili Shi, Yu Miao, Huamin Yang, and Zhengang Jiang "Lung fields segmentation in chest radiographs using Dense-U-Net and fully connected CRF", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172011 (27 January 2021); https://doi.org/10.1117/12.2589384

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