{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T10:20:29Z","timestamp":1782123629808,"version":"3.54.5"},"reference-count":25,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002527","name":"Kyonggi University","doi-asserted-by":"publisher","award":["Kyonggi University Research Grant 2019"],"award-info":[{"award-number":["Kyonggi University Research Grant 2019"]}],"id":[{"id":"10.13039\/501100002527","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate identification of the boundaries of organs or abnormal objects (e.g., tumors) in medical images is important in surgical planning and in the diagnosis and prognosis of diseases. In this study, we propose a deep learning-based method to segment lung areas in chest X-rays. The novel aspect of the proposed method is the self-attention module, where the outputs of the channel and spatial attention modules are combined to generate attention maps, with each highlighting those regions of feature maps that correspond to \u201cwhat\u201d and \u201cwhere\u201d to attend in the learning process, respectively. Thereafter, the attention maps are multiplied element-wise with the input feature map, and the intermediate results are added to the input feature map again for residual learning. Using X-ray images collected from public datasets for training and evaluation, we applied the proposed attention modules to U-Net for segmentation of lung areas and conducted experiments while changing the locations of the attention modules in the baseline network. The experimental results showed that our method achieved comparable or better performance than the existing medical image segmentation networks in terms of Dice score when the proposed attention modules were placed in lower layers of both the contracting and expanding paths of U-Net.<\/jats:p>","DOI":"10.3390\/s21020369","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T23:03:42Z","timestamp":1610319822000},"page":"369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network"],"prefix":"10.3390","volume":"21","author":[{"given":"Minki","family":"Kim","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Kyonggi University, Gyeonggi-do 16227, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byoung-Dai","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Kyonggi University, Gyeonggi-do 16227, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_2","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening","volume":"557","author":"McKinney","year":"2020","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37749","DOI":"10.1109\/ACCESS.2019.2900053","article-title":"Automatic cardiothoracic ratio calculation with deep learning","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_6","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the 15th European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Saad, M.N., Muda, Z., Ashaari, N.S., and Hamid, H.A. (2014, January 28\u201330). Image segmentation for lung region in chest X-ray images using edge detection and morphology. Proceedings of the 2014 IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2014.7072687"},{"key":"ref_10","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv."},{"key":"ref_11","unstructured":"Gaal, G., Maga, B., and Lukacs, A. (2020). Attention U-Net based adversarial architectures for chest X-ray lung segmentation. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dai, W., Doyle, J., Liang, X., Zhang, H., Dong, N., Li, Y., and Xing, E.P. (2017). SCAN: Structure correcting adversarial network for organ segmentation in chest X-rays. arXiv.","DOI":"10.1007\/978-3-030-00889-5_30"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_14","unstructured":"Tang, Y., Tang, Y., Xiao, J., and Summers, R.M. (2019). XLSor: A robust and accurate lung segmentor on chest X-rays using criss-cross attention and customized radiorealistic abnormalities generation. arXiv."},{"key":"ref_15","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (November, January 27). Ccnet: Criss-cross attention for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2019, January 16\u201320). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_19","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018, January 3\u20136). Pyramid attention network for semantic segmentation. Proceedings of the British Machine Vision Conference, Newcastle, UK."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yu, Q., Xie, L., Wang, Y., Zhou, Y., Fishman, E.K., and Yuille, A.L. (2018, January 18\u201322). Recurrent saliency transformation network: Incorporating multi-stage visual cues for small organ segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00864"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_22","unstructured":"(2020, August 21). Tuberculosis Chest X-ray Image Data Sets, Available online: https:\/\/lhncbc.nlm.nih.gov\/publication\/pub9931."},{"key":"ref_23","unstructured":"(2020, August 21). Digital Image Database. Available online: http:\/\/db.jsrt.or.jp\/eng.php."},{"key":"ref_24","unstructured":"(2020, December 07). Open Access Biomedical Image Search Engine, Available online: http:\/\/archive.nlm.nih.gov\/repos\/chestImages.php."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Stirenko, S., Kochura, Y., Alienin, O., Rokovyi, O., Gang, P., Zeng, W., and Gordienko, Y. (2018, January 24\u201326). Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation. Proceedings of the IEEE 38th International Conference on Electronics and Nanotechnology, Kiev, Ukraine.","DOI":"10.1109\/ELNANO.2018.8477564"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/369\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:08:16Z","timestamp":1760159296000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,7]]},"references-count":25,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21020369"],"URL":"https:\/\/doi.org\/10.3390\/s21020369","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,7]]}}}