Abstract
Lung lobe segmentation in chest CT has been used for the analysis of lung functions and surgical planning. However, accurate lobe segmentation is difficult as 80% of patients have incomplete and/or fake fissures. Furthermore, lung diseases such as chronic obstructive pulmonary disease (COPD) can increase the difficulty of differentiating the lobar fissures. Lobar fissures have similar intensities to those of the vessels and airway wall, which could lead to segmentation error in automated segmentation. In this study, a fully automated lung lobe segmentation method with 3D U-Net was developed and validated with internal and external datasets. The volumetric chest CT scans of 196 normal and mild-to-moderate COPD patients from three centers were obtained. Each scan was segmented using a conventional image processing method and manually corrected by an expert thoracic radiologist to create gold standards. The lobe regions in the CT images were then segmented using a 3D U-Net architecture with a deep convolutional neural network (CNN) using separate training, validation, and test datasets. In addition, 40 independent external CT images were used to evaluate the model. The segmentation results for both the conventional and deep learning methods were compared quantitatively to the gold standards using four accuracy metrics including the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), mean surface distance (MSD), and Hausdorff surface distance (HSD). In internal validation, the segmentation method achieved high accuracy for the DSC, JSC, MSD, and HSD (0.97 ± 0.02, 0.94 ± 0.03, 0.69 ± 0.36, and 17.12 ± 11.07, respectively). In external validation, high accuracy was also obtained for the DSC, JSC, MSD, and HSD (0.96 ± 0.02, 0.92 ± 0.04, 1.31 ± 0.56, and 27.89 ± 7.50, respectively). This method took 6.49 ± 1.19 s and 8.61 ± 1.08 s for lobe segmentation of the left and right lungs, respectively. Although various automatic lung lobe segmentation methods have been developed, it is difficult to develop a robust segmentation method. However, the deep learning–based 3D U-Net method showed reasonable segmentation accuracy and computational time. In addition, this method could be adapted and applied to severe lung diseases in a clinical workflow.
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References
Doel T, Gavaghan DJ, Grau V: Review of automatic pulmonary lobe segmentation methods from CT. Comput Med Imaging Graph 40:13–29, 2015
Jeffery PK: Structural and inflammatory changes in COPD: a comparison with asthma. Thorax 53(2):129–136, 1998
Leung AN: Pulmonary tuberculosis: The essentials. Radiology 210(2):307–322, 1999
Morgan EJ: Silicosis and tuberculosis. Chest 75(2):202–203, 1979
European, R. S., & American Thoracic Society: American Thoracic Society/European Respiratory Society international multidisciplinary consensus classification of the idiopathic interstitial pneumonias. This joint statement of the American Thoracic Society (ATS), and the European Respiratory Society (ERS) was adopted by the ATS board of directors, June 2001 and by the ERS executive committee, June 2001. Am J Respir Crit Care Med 165(2):277, 2002
Lassen B, Kuhnigk JM, Friman O, Krass S, Peitgen HO: Automatic segmentation of lung lobes in CT images based on fissures, vessels, and bronchi. In Biomedical imaging: From Nano to macro, 2010 IEEE international symposium on. IEEE, 2010, pp 560–563.
Lassen B, Kuhnigk JM, Schmidt M, Krass S, Peitgen HO: Lung and lung lobe segmentation methods at Fraunhofer MEVIS. In: Fourth international workshop on pulmonary image analysis, vol. 18. 2011, pp 185–99
Zhang L, Hoffman EA, Reinhardt JM: Atlas-driven lung lobe segmentation in volumetric X-ray CT images. IEEE Trans Med Imaging 25(1):1–16, 2006
Pu J, Zheng B, Leader JK, Fuhrman C, Knollmann F, Klym A, Gur D: Pulmonary lobe segmentation in CT examinations using implicit surface fitting. IEEE Trans Med Imaging 28(12):1986–1996, 2009
Kuhnigk JM, Hahn H, Hindennach M, Dicken V, Krass S, Peitgen HO: Lung lobe segmentation by anatomy-guided 3D watershed transform. In: Medical Imaging 2003: Image Processing, vol. 5032. International Society for Optics and Photonics, 2003, pp 1482–1491
Zhang L, Hoffman EA, Reinhardt JM: Lung lobe segmentation by graph search with 3D shape constraints. In: Medical Imaging 2001: Physiology and Function from Multidimensional Images, vol. 4321. International Society for Optics and Photonics, 2001, pp 204–216
Harrison AP, Xu Z, George K, Lu L, Summers RM, Mollura DJ:. Progressive and multi-path holistically nested neural networks for pathological lung segmentation from CT images. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer, 2017, pp 621–629
George K, Harrison AP, Jin D, Xu Z, Mollura DJ: Pathological pulmonary lobe segmentation from CT images using progressive holistically nested neural networks and random Walker. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer, 2017, pp. 195–203
Meenakshi S, Manjunath KY, Balasubramanyam V: Morphological variations of the lung fissures and lobes. Indian J Chest Dis Allied Sci 46:179–182, 2004
Leader JK, Zheng B, Rogers RM, Sciurba FC, Perez A, Chapman BE, … Gur D: Automated lung segmentation in X-ray computed tomography: Development and evaluation of a heuristic threshold-based scheme 1. Acad Radiol 10(11):1224–1236,2003
Duda RO, Hart PE: Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15, 1972
Lee YJ, Lee M, Kim N, Seo JB, Park JY: Automatic left and right lung separation using free-formed surface fitting on volumetric CT. J Digit Imaging 27(4):538–547, 2014
D’Errico J: Surface fitting using Gridfit. In: Matlab Central File Exchange. 2006
Dillencourt MB, Samet H, Tamminen M: A general approach to connected-component labeling for arbitrary image representations. J ACM (JACM) 39(2):253–280, 1992
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O: 3D U-net: Learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention. Cham: Springer, 2016, pp 424–432
Ronneberger O, Fischer P, Brox T: U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer, 2015, pp 234–241
Long J, Shelhamer E, Darrell T: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, pp 3431–3440
Sørensen T: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biol Skr 5:1–34, 1948
Bae J, Kim N, Lee SM, Seo JB, Kim HC: Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT. Med Phys 41(4), 2014
Gallagher E: COMPAH documentation. 1999. User’s Guide and application published at: http://www.es.umb.edu/edgwebp.htm
Rockafellar RT, Wets RJB: Variational analysis. Springer-Verlag, 2005, p 117. ISBN 3-540-62772-3
Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: NHLBI/WHO global initiative for chronic obstructive lung disease (GOLD) workshop summary. Am J Respir Crit Care Med 163(5):1256–1276, 2001
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This work was supported by the Industrial Strategic technology development program (10072064) funded by the Ministry of Trade Industry and Energy (MI, Korea).
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Park, J., Yun, J., Kim, N. et al. Fully Automated Lung Lobe Segmentation in Volumetric Chest CT with 3D U-Net: Validation with Intra- and Extra-Datasets. J Digit Imaging 33, 221–230 (2020). https://doi.org/10.1007/s10278-019-00223-1
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DOI: https://doi.org/10.1007/s10278-019-00223-1