{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:36:13Z","timestamp":1774488973702,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"S14","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:00:00Z","timestamp":1607990400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with <jats:inline-formula><jats:alternatives><jats:tex-math>$$0.93\\pm 0.13$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mn>0.93<\/mml:mn>\n                      <mml:mo>\u00b1<\/mml:mo>\n                      <mml:mn>0.13<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and dice similarity coefficient (DSC) with <jats:inline-formula><jats:alternatives><jats:tex-math>$$0.92\\pm 0.14$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mn>0.92<\/mml:mn>\n                      <mml:mo>\u00b1<\/mml:mo>\n                      <mml:mn>0.14<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, and achieves competitive performance on diagnostic accuracy with 93.45% and <jats:inline-formula><jats:alternatives><jats:tex-math>$$F_1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msub>\n                      <mml:mi>F<\/mml:mi>\n                      <mml:mn>1<\/mml:mn>\n                    <\/mml:msub>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>-score with 92.97%.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-020-01325-5","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T17:04:06Z","timestamp":1608051846000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study"],"prefix":"10.1186","volume":"20","author":[{"given":"Qingfeng","family":"Wang","sequence":"first","affiliation":[]},{"given":"Qiyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guoting","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Zhiqin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yuwei","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Weiyun","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jie-Zhi","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,12,15]]},"reference":[{"issue":"Suppl 2","key":"1325_CR1","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1136\/thx.2010.136986","volume":"65","author":"A MacDuff","year":"2010","unstructured":"MacDuff A, Arnold A, Harvey J. Management of spontaneous pneumothorax: British thoracic society pleural disease guideline 2010. Thorax. 2010;65(Suppl 2):18\u201331. https:\/\/doi.org\/10.1136\/thx.2010.136986.","journal-title":"Thorax"},{"key":"1325_CR2","doi-asserted-by":"publisher","unstructured":"Suthar M, Mahjoubfar A, Seals K, Lee EW, Jalaii B. Diagnostic tool for pneumothorax. In: 2016 IEEE photonics society summer topical meeting series (SUM); 2016. p. 218\u20139. https:\/\/doi.org\/10.1109\/PHOSST.2016.7548806.","DOI":"10.1109\/PHOSST.2016.7548806"},{"key":"1325_CR3","first-page":"1505","volume":"2011","author":"AP Wakai","year":"2011","unstructured":"Wakai AP. Spontaneous pneumothorax. BMJ Clin Evid. 2011;2011:1505.","journal-title":"BMJ Clin Evid"},{"issue":"11","key":"1325_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pmed.1002697","volume":"15","author":"AG Taylor","year":"2018","unstructured":"Taylor AG, Mielke C, Mongan J. Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study. PLOS Med. 2018;15(11):1\u201315. https:\/\/doi.org\/10.1371\/journal.pmed.1002697.","journal-title":"PLOS Med"},{"key":"1325_CR5","unstructured":"Weber T, Bayer F, Haas W, Pelzer G, Rieger J, Ritter A, Wucherer L, Braun JM, Durst J, Michel T, Anton G. Investigation of the signature of lung tissue in X-ray grating-based phase-contrast imaging. arXiv e-prints; 2012. p. 1212\u20135031. arXiv:1212.5031."},{"key":"1325_CR6","unstructured":"Geva O, Zimmerman-Moreno G, Lieberman S, Konen E, Greenspan H. Pneumothorax detection in chest radiographs using local and global texture signatures. In: Hadjiiski LM, Tourassi GD, editors. Medical imaging 2015: computer-aided diagnosis, 2015; vol. 9414. p. 448\u201354."},{"key":"1325_CR7","first-page":"2908517","volume":"2018","author":"Y-H Chan","year":"2018","unstructured":"Chan Y-H, Zeng Y-Z, Wu H-C, Wu M-C, Sun H-M. Effective pneumothorax detection for chest X-ray images using local binary pattern and support vector machine. J Healthc Eng. 2018;2018:2908517.","journal-title":"J Healthc Eng"},{"key":"1325_CR8","doi-asserted-by":"publisher","unstructured":"Wang Q, Cheng J, Liu Z, Huang J, Liu Q, Zhou Y, Xu W, Wang C, Zhou X. Multi-order transfer learning for pathologic diagnosis of pulmonary nodule malignancy. In: IEEE international conference on bioinformatics and biomedicine (BIBM), 2018. p. 2813\u20135. https:\/\/doi.org\/10.1109\/BIBM.2018.8621407.","DOI":"10.1109\/BIBM.2018.8621407"},{"key":"1325_CR9","doi-asserted-by":"publisher","unstructured":"Wang Q, Huang J, Liu Z, Cheng J, Zhou Y, Liu Q, Wang Y, Zhou X, Wang C. Higher-order transfer learning for pulmonary nodule attribute prediction in chest CT images. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM); 2019. p. 741\u20135. https:\/\/doi.org\/10.1109\/BIBM47256.2019.8983299.","DOI":"10.1109\/BIBM47256.2019.8983299"},{"key":"1325_CR10","doi-asserted-by":"publisher","unstructured":"Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR); 2017. p. 3462\u201371. https:\/\/doi.org\/10.1109\/CVPR.2017.369.","DOI":"10.1109\/CVPR.2017.369"},{"key":"1325_CR11","unstructured":"Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding DY, Bagul A, Langlotz C, Shpanskaya KS, Lungren MP, Ng AY. Chexnet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv e-prints; 2017. arXiv:1711.05225."},{"key":"1325_CR12","unstructured":"Yao L, Poblenz E, Dagunts D, Covington B, Bernard D, Lyman K. Learning to diagnose from scratch by exploiting dependencies among labels. arXiv e-prints; 2017;1710\u201310501. arXiv:1710.10501."},{"key":"1325_CR13","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/978-3-030-13469-3_88","volume-title":"Progress in pattern recognition, image analysis, computer vision, and applications","author":"S G\u00fcndel","year":"2019","unstructured":"G\u00fcndel S, Grbic S, Georgescu B, Liu S, Maier A, Comaniciu D. Learning to recognize abnormalities in chest X-rays with location-aware dense networks. In: Vera-Rodriguez R, Fierrez J, Morales A, editors. Progress in pattern recognition, image analysis, computer vision, and applications. Cham: Springer; 2019. p. 757\u201365."},{"key":"1325_CR14","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/978-3-030-04239-4_38","volume-title":"Neural information processing","author":"Q Wang","year":"2018","unstructured":"Wang Q, Cheng J-Z, Zhou Y, Zhuang H, Li C, Chen B, Liu Z, Huang J, Wang C, Zhou X. Low-shot multi-label incremental learning for thoracic diseases diagnosis. In: Cheng L, Leung ACS, Ozawa S, editors. Neural information processing. Cham: Springer; 2018. p. 420\u201332."},{"key":"1325_CR15","unstructured":"Jun TJ, Kim D, Kim D. Automated diagnosis of pneumothorax using an ensemble of convolutional neural networks with multi-sized chest radiography images. arXiv e-prints; 2018;1804\u201306821. arXiv:1804.06821."},{"issue":"1","key":"1325_CR16","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1148\/radiol.2018180921","volume":"291","author":"M Annarumma","year":"2019","unstructured":"Annarumma M, Withey SJ, Bakewell RJ, Pesce E, Goh V, Montana G. Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology. 2019;291(1):196\u2013202. https:\/\/doi.org\/10.1148\/radiol.2018180921.","journal-title":"Radiology"},{"issue":"4","key":"1325_CR17","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):640\u201351. https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1325_CR18","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical image computing and computer-assisted intervention\u2014MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention\u2014MICCAI 2015. Cham: Springer; 2015. p. 234\u201341."},{"key":"1325_CR19","doi-asserted-by":"publisher","unstructured":"Novikov AA, Lenis D, Major D, Hlad$$\\mathring{{{\\rm u}}}$$vka J, Wimmer M, B\u00fchler K. Fully convolutional architectures for multiclass segmentation in chest radiographs. IEEE Trans Med Imaging. 2018;37(8):1865\u201376. https:\/\/doi.org\/10.1109\/TMI.2018.2806086.","DOI":"10.1109\/TMI.2018.2806086"},{"key":"1325_CR20","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P-M, Larochelle H. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18\u201331. https:\/\/doi.org\/10.1016\/j.media.2016.05.004.","journal-title":"Med Image Anal"},{"key":"1325_CR21","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1007\/978-3-030-00934-2_61","volume-title":"Medical image computing and computer assisted intervention\u2014MICCAI 2018","author":"N Dong","year":"2018","unstructured":"Dong N, Kampffmeyer M, Liang X, Wang Z, Dai W, Xing E. Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-L\u00f3pez C, Fichtinger G, editors. Medical image computing and computer assisted intervention\u2014MICCAI 2018. Cham: Springer; 2018. p. 544\u201352."},{"key":"1325_CR22","doi-asserted-by":"publisher","first-page":"18450","DOI":"10.1109\/ACCESS.2019.2896409","volume":"7","author":"Q Wang","year":"2019","unstructured":"Wang Q, Zhou X, Wang C, Liu Z, Huang J, Zhou Y, Li C, Zhuang H, Cheng J. Wgan-based synthetic minority over-sampling technique: Improving semantic fine-grained classification for lung nodules in ct images. IEEE Access. 2019;7:18450\u201363. https:\/\/doi.org\/10.1109\/ACCESS.2019.2896409.","journal-title":"IEEE Access"},{"key":"1325_CR23","doi-asserted-by":"publisher","unstructured":"Luo G, Liu Z, Wang Q, Liu Q, Zhou Y, Xu W, Huang J, Fu J, Cheng J. Fully convolutional multi-scale ScSE-DenseNet for automatic pneumothorax segmentation in chest radiographs. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM); 2019. p. 1551\u20135. https:\/\/doi.org\/10.1109\/BIBM47256.2019.8983004.","DOI":"10.1109\/BIBM47256.2019.8983004"},{"key":"1325_CR24","doi-asserted-by":"publisher","unstructured":"Huang G, Liu ZVD, Maaten, L, Weinberger KQ. Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR); 2017. p. 2261\u20139. https:\/\/doi.org\/10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1325_CR25","doi-asserted-by":"publisher","unstructured":"J\u00e9gou S, Drozdzal M, Vazquez D, Romero A, Bengio Y. The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW); 2017. p. 1175\u20131183. https:\/\/doi.org\/10.1109\/CVPRW.2017.156.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"1325_CR26","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu Wei, Jia Yangqing, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR); 2015. p. 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1325_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372","author":"J Hu","year":"2019","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E. Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell. 2019;. https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"1325_CR28","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1109\/TMI.2018.2867261","volume":"38","author":"AG Roy","year":"2019","unstructured":"Roy AG, Navab N, Wachinger C. Recalibrating fully convolutional networks with spatial and channel \u201csqueeze and excitation\u201d blocks. IEEE Trans Med Imaging. 2019;38(2):540\u20139. https:\/\/doi.org\/10.1109\/TMI.2018.2867261.","journal-title":"IEEE Trans Med Imaging"},{"key":"1325_CR29","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi S. V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 4th international conference on 3D vision (3DV); 2016. p. 565\u201371. https:\/\/doi.org\/10.1109\/3DV.2016.79.","DOI":"10.1109\/3DV.2016.79"},{"key":"1325_CR30","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","volume":"51","author":"M Khened","year":"2019","unstructured":"Khened M, Kollerathu VA, Krishnamurthi G. Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med Image Anal. 2019;51:21\u201345. https:\/\/doi.org\/10.1016\/j.media.2018.10.004.","journal-title":"Med Image Anal"},{"key":"1325_CR31","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: 2015 IEEE international conference on computer vision (ICCV); 2015. p. 1026\u201334. https:\/\/doi.org\/10.1109\/ICCV.2015.123.","DOI":"10.1109\/ICCV.2015.123"},{"issue":"12","key":"1325_CR32","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder\u2013decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2481\u201395. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1325_CR33","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: The European conference on computer vision (ECCV); 2018. p. 1\u201318.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1325_CR34","doi-asserted-by":"crossref","unstructured":"Yang M, Yu K, Zhang C, Li Z, Yang K. Denseaspp for semantic segmentation in street scenes. In: The IEEE conference on computer vision and pattern recognition (CVPR); 2018. p. 3684\u201392.","DOI":"10.1109\/CVPR.2018.00388"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01325-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-020-01325-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01325-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T17:07:41Z","timestamp":1608052061000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-01325-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":34,"journal-issue":{"issue":"S14","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1325"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-01325-5","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12]]},"assertion":[{"value":"15 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was approved by the ethics committee of Mianyang Central Hospital.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"317"}}