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RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The robust and automatic segmentation of the pulmonary lobe is vital to surgical planning and regional image analysis of pulmonary related diseases in real-time Computer Aided Diagnosis systems. While a number of studies have examined this issue, the segmentation of unclear borders of the five lobes of the lung remains challenging because of incomplete fissures, the diversity of anatomical pulmonary information, and obstructive lesions caused by pulmonary diseases. This study proposes a model called Regularized Pulmonary Lobe Segmentation Network to accurately predict the lobes as well as the borders.

Methods

First, a 3D fully convolutional network is constructed to extract contextual features from computed tomography images. Second, multi-task learning is employed to learn the segmentations of the lobes and the borders between them to train the neural network to better predict the borders via shared representation. Third, a 3D depth-wise separable de-convolution block is proposed for deep supervision to efficiently train the network. We also propose a hybrid loss function by combining cross-entropy loss with focal loss using adaptive parameters to focus on the tissues and the borders of the lobes.

Results

Experiments are conducted on a dataset annotated by experienced clinical radiologists. A 4-fold cross-validation result demonstrates that the proposed approach can achieve a mean dice coefficient of 0.9421 and average symmetric surface distance of 1.3546 mm, which is comparable to state of the art methods. The proposed approach has the capability to accurately segment voxels that are near the lung wall and fissure.

Conclusion

In this paper, a 3D fully convolutional networks framework is proposed to segment pulmonary lobes in chest CT images accurately. Experimental results show the effectiveness of the proposed approach in segmenting the tissues as well as the borders of the lobes.

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Notes

  1. https://chestimagingplatform.org.

References

  1. Doel T, Gavaghan David J, Grau V (2015) Review of automatic pulmonary lobe segmentation methods from ct. Comput Med Imaging Graph 40:13–29

    Article  Google Scholar 

  2. Xiuyuan X, Chengdi W, Jixiang G, Lan Y, Hongli B, Weimin L, Zhang Y (2020) Deepln: a framework for automatic lung nodule detection using multi-resolution ct screening images. Knowl Based Syst 189:105128

    Article  Google Scholar 

  3. Li G, Shan J, Zhiyong Y, Guobin Z, Wang L (2019) Automated pulmonary nodule detection in ct images using 3D deep squeeze-and-excitation networks. Int J Comput Assist Radiol Surg 14(11):1969–1979

    Article  Google Scholar 

  4. Atsushi T, Hiroshi F (2013) Fast lung nodule detection in chest ct images using cylindrical nodule-enhancement filter. Int J Comput Assist Radiol Surg 8(2):193–205

    Article  Google Scholar 

  5. Cronin P, Gross Barry H, Aine MK, Patel S, Kazerooni Ella A, Carlos Ruth C (2010) Normal and accessory fissures of the lung: evaluation with contiguous volumetric thin-section multidetector ct. Eur J Radiol 75(2):e1–e8

    Article  Google Scholar 

  6. Kuhnigk JM, Hahn H, Hindennach M, Dicken V, Krass S, Peitgen HO (2003) Lung lobe segmentation by anatomy-guided 3D watershed transform. In: Medical imaging 2003: image processing. International Society for Optics and Photonics, vol 5032, pp 1482–1490

  7. Ukil S, Hoffman EA, Reinhardt JM (2005) Automatic lung lobe segmentation in x-ray CT images by 3D watershed transform using anatomic information from the segmented airway tree. In: Medical imaging 2005: image processing, vol 5747, p 556

  8. Ukil S, Reinhardt JM (2009) Anatomy-guided lung lobe segmentation and fissure analysis in x-ray CT images. IEEE Trans Med Imaging 28(2):202–214

    Article  Google Scholar 

  9. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  10. George K, Harrison AP, Jin D, Xu Z, Mollura DJ (2017) 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. Springer, pp 195–203

  11. Harrison AP, Xu Z, George K, Lu L, Summers RM, Mollura DJ (2017) 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. Springer, pp 621–629

  12. Park J, Yun J, Kim N, Park B, Cho Y, Hee JP, Song M, Lee M, Joon BS (2020) Fully automated lung lobe segmentation in volumetric chest ct with 3D U-Net: validation with intra-and extra-datasets. J Digit Imaging 33(1):221–230

    Article  Google Scholar 

  13. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 2424–432

  14. Imran AAZ, Hatamizadeh A, Ananth SP, Ding X, Terzopoulos D, Tajbakhsh N (2018) Automatic segmentation of pulmonary lobes using a progressive dense v-network. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 11045 LNCS(Lll), pp 282–290

  15. Ferreira FT, Sousa P, Galdran A, Sousa MR, Campilho A (2018) End-to-end supervised lung lobe segmentation. In: Proceedings of the international joint conference on neural networks, 2018, pp 4763–4770

  16. Tang H, Zhang C, Xie X (2019) Automatic pulmonary lobe segmentation using deep learning. In: Proceedings: international symposium on biomedical imaging, 2019-April, pp 1225–1228

  17. Lee H, Matin T, Gleeson F, Grau V (2019) Efficient 3D fully convolutional networks for pulmonary lobe segmentation in ct images. arXiv:1909.07474

  18. Gerard SE, Reinhardt JM (2019) Pulmonary lobe segmentation using a sequence of convolutional neural networks for marginal learning. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, pp 1207–1211

  19. Gerard ES, Patton JT, Christensen EG, Bayouth EJ, Reinhardt MJ (2018) Fissurenet: a deep learning approach for pulmonary fissure detection in ct images. IEEE Trans Med Imaging 38(1):156–166

    Article  Google Scholar 

  20. Setio AAA, Traverso A, De Bel T, Berens MSN, van den Bogaard C, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B et al (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Medical Image Anal 42:1–13

    Article  Google Scholar 

  21. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille Alan L (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  22. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  23. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  24. Rich C (1997) Multitask learning. Mach Learn 28(1):41–75

    Article  Google Scholar 

  25. Lin T-Y , Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988

  26. Dou Q, Chen H, Jin Y, Yu L, Qin J, Heng P-A (2016) 3D deeply supervised network for automatic liver segmentation from ct volumes. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 149–157

  27. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch. In: NIPS 2017 workshop autodiff submission

  28. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Yoshua B, Yann L (eds) 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, conference track proceedings

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Acknowledgements

This work was supported by the National Major Science and Technology Projects of China under Grant 2018AAA0100201, the Major Science and Technology Project from the Science & Technology Department of Sichuan Province under Grant 2020YFG0473 and by the Science and Technology Project of Chengdu, PR China under Grant 2017-CY02-00030-GX.

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan Province, People’s Republic of China

    Jinxin Liu, Jixiang Guo, Xiuyuan Xu & Zhang Yi

  2. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, People’s Republic of China

    Chengdi Wang, Jun Shao & Weimin Li

  3. Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, People’s Republic of China

    Xiaoxin Liu & Hongxia Li

Authors
  1. Jinxin Liu
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  2. Chengdi Wang
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  3. Jixiang Guo
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  4. Jun Shao
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  5. Xiuyuan Xu
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  6. Xiaoxin Liu
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  7. Hongxia Li
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  8. Weimin Li
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  9. Zhang Yi
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Correspondence to Weimin Li or Zhang Yi.

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Liu, J., Wang, C., Guo, J. et al. RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning. Int J CARS 16, 895–904 (2021). https://doi.org/10.1007/s11548-021-02360-x

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  • DOI: https://doi.org/10.1007/s11548-021-02360-x

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