@inproceedings{4c4a7767ee3d4408bc78c631a6a95df7,
title = "A Novel Lesion Segmentation Algorithm based on U-Net Network for Tuberculosis CT Image",
abstract = "Lung CT images provide several essential information for lung disease diagnosis and lung surgery. However, the traditional detection method through manual segmentation is laborious and time-consuming. This paper presents automatic tuberculosis (TB) lesion segmentation method based on U-Net neural network for detecting TB. In addition, we combined an edge detection algorithm called canny edge detector with this network to get a more accurate TB lesion boundary. This method is trained on two split databases with 3576 lung CT images obtained by data enhancement on 447 discontinuous lung CT images. The results show that the proposed approach is validated for complex TB lesions with a high dice coefficient (91.2%).",
keywords = "Image processing, Lesions segmentation, Tuberculosis, Unet",
author = "Shaoyue Wen and Jing Liu and Wenge Xu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021 ; Conference date: 14-10-2021 Through 17-10-2021",
year = "2021",
doi = "10.1109/ICCAIS52680.2021.9624633",
language = "English",
series = "10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "909--914",
booktitle = "10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021 - Proceedings",
}
