Non-local attention and multi-task learning based lung segmentation in chest X-ray_Journal of Biomedical Engineering

Authors：

XIONG Liang ^1,2 , QIN Xiaolin ^1,2 ,  LIU Xin ³

1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, P. R. China;
2. University of Chinese Academy of Sciences, Beijing 100049, P. R. China;
3. Dongguan University of Technology, Dongguan, Guangdong 523808, P. R. China;

Corresponding author：

LIU Xin, Email: yemoreliu@outlook.com

Keywords：

Chest X-ray; Lung segmentation; Non-local attention; Multi-task learning

DOI：

10.7507/1001-5515.202211079

Video：

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Abstract Full text Figures/Tables Video References Cited by

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.

Citation： XIONG Liang, QIN Xiaolin, LIU Xin. Non-local attention and multi-task learning based lung segmentation in chest X-ray. Journal of Biomedical Engineering, 2023, 40(5): 912-919. doi: 10.7507/1001-5515.202211079 Copy

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28.	Shiraishi J, Katsuragawa S, Ikezoe J, et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am J Roentgenol, 2000, 174(1): 71-74.
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1. Viergever M A, Romeny B H, Goudoever J V. Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging, 2001, 20(12): 1228-1241.
2. Hasan M J, Alom M S, Ali M S. Deep learning based detection and segmentation of COVID-19 & Pneumonia on chest X-ray image// 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD). Dhaka: IEEE, 2021: 210-214.
3. Abdulah H, Huber B, Lal S, et al. Lung segmentation in chest X-rays with Res-CR-Net. arXiv, 2020: 2011.08655.
4. Tseng L Y, Huang L C. An adaptive thresholding method for automatic lung segmentation in CT images// Africon 2009. Nairobi: IEEE, 2009: 1-5.
5. Yan Z, Cheng X. Medical image segmentation based on watershed and graph theory// 2010 3rd International Congress on Image and Signal Processing. Yantai: IEEE, 2010: 1419-1422.
6. Saad M N, Muda Z, Ashaari N S, et al. Image segmentation for lung region in chest X-ray images using edge detection and morphology// 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014). Penang: IEEE, 2014: 46-51.
7. Ginneken B V, Stegmann M B, Loog M. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med Image Anal, 2006, 10(1): 19-40.
8. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2015, 39(4): 640-651.
9. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation// 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Munich: Medical Image Computing and Computer Assisted Intervention Society, 2015: 234-241.
10. Hooda R, Mittal A, Sofat S. An efficient variant of fully-convolutional network for segmenting lung fields from chest radiographs. Wireless Pers Commun, 2018, 101(3): 1559-1579.
11. Rashid R, Akram M U, Hassan T. Fully convolutional neural network for lungs segmentation from chest X-Rays// 15th International Conference Image Analysis and Recognition (ICIAR). Póvoa de Varzim: Association for Image and Machine Intelligence, 2018: 71-80.
12. Chen H, Qi X, Yu L, et al. DCAN: Deep contour-aware networks for accurate gland segmentation// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 2487-2496.
13. Kholiavchenko M, Sirazitdinov I, Kubrak K, et al. Contour-aware multi-label chest X-ray organ segmentation. Int J Comput Ass Rad, 2020, 15(3): 425-436.
14. Chen L, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation// 15th European Conference on Computer Vision (ECCV). Munich: ECCV, 2018: 833-851.
15. Fu J, Liu J, Tian H, et al. Dual attention network for scene segmentation// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). California: IEEE, 2019: 3141-3149.
16. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: Learning where to look for the pancreas. arXiv, 2018: 1804.03999.
17. Wang X, Girshick R, Gupta A, et al. Non-local neural networks// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 7794-7803.
18. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need// 31st Conference on Neural Information Processing Systems (NeurIPS). Long Beach: Neural Information Processing Systems Foundation, 2017: 6000-6010.
19. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv, 2021: 2010.11929.
20. Carion N, Massa F, Synnaeve G N, et al. End-to-end object detection with transformers// 16th European Conference on Computer Vision (ECCV). Glasgow: ECCV, 2020: 213-229.
21. Chen J, Lu Y, Yu Q, et al. TransUNet: transformers make strong encoders for medical image segmentation. arXiv, 2021: 2102.04306.
22. Zhen Mingmin, Wang Jinglu, Zhou Lei, et al. Joint semantic segmentation and boundary detection using iterative pyramid contexts// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 13663-13672.
23. Ding Henghui, Jiang Xudong, Liu Aiqun, et al. Boundary-aware feature propagation for scene segmentation// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019: 6818-6828.
24. Hong J L, Kim J U, Lee S, et al. Structure boundary preserving segmentation for medical image with ambiguous boundary// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 4816-4825.
25. Wang J, Wei L, Wang L, et al. Boundary-aware transformers for skin lesion segmentation// 21th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Marleen de Bruijne: Medical Image Computing and Computer Assisted Intervention Society, 2021: 206-216.
26. He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
27. Canny J F. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell, 1986(6): 679-698.
28. Shiraishi J, Katsuragawa S, Ikezoe J, et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am J Roentgenol, 2000, 174(1): 71-74.
29. Jaeger S, Candemir S, Antani S, et al. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imag Med Surg, 2014, 4(6): 475-477.

Journal of Biomedical Engineering

Non-local attention and multi-task learning based lung segmentation in chest X-ray

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