AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
Source abstract
Abstract
Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly available LUNA16 and LIDC-IDRI datasets, respectively.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Lung Cancer Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain
National Subsea Centre, Robert Gordon University, Aberdeen AB10 7GJ, UK
Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt
References (44)
Show source-provided references
1. (2021, August 08). Cancer. Available online: https://www.who.int/news-room/fact-sheets/detail/cancer/.
2. (2021, August 08). World Lung Cancer Day 2020 Fact Sheet. Available online: https://www.chestnet.org/newsroom/chest-news/2020/07/world-lung-cancer-day-2020-fact-sheet/.
3. The National Lung Screening Trial Research Team (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med., 365, 395–409.10.1056/NEJMoa1102873 ↗
4. Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation10.2196/16709 ↗
5. (2021, August 08). Seattle Cancer Care Alliance Proton Therapy Center. Available online: https://www.sccaprotontherapy.com/cancers-treated/lung-cancer-treatment.
6. British Thoracic Society guidelines for the investigation and management of pulmonary nodules: Accredited by NICE10.1136/thoraxjnl-2015-207168 ↗
7. iW-Net: An automatic and minimalistic interactive lung nodule segmentation deep network10.1038/s41598-019-48004-8 ↗
8. Keetha, N.V., Babu P, S.A., and Annavarapu, C.S.R. (2020). U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation. arXiv.
9. Dual-branch residual network for lung nodule segmentation10.1016/j.asoc.2019.105934 ↗
10. Wu, B., Zhou, Z., Wang, J., and Wang, Y. (2018, January 4–7). Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.10.1109/ISBI.2018.8363765 ↗
11. Tang, H., Zhang, C., and Xie, X. (2019, January 13–17). Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation. Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China.10.1007/978-3-030-32226-7_30 ↗
12. Kumar Singh, V., Abdel-Nasser, M., Pandey, N., and Puig, D. (2021). Lunginfseg: Segmenting COVID-19 infected regions in lung ct images based on a receptive-field-aware deep learning framework. Diagnostics, 11.10.3390/diagnostics11020158 ↗
13. Multiple resolution residually connected feature streams for automatic lung tumor segmentation from CT images10.1109/TMI.2018.2857800 ↗
14. Segmentation of pulmonary nodules in thoracic CT scans: A region growing approach10.1109/TMI.2007.907555 ↗
15. Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field10.1118/1.4793409 ↗
16. A novel approach for lung nodules segmentation in chest CT using level sets10.1109/TIP.2013.2282899 ↗
17. A novel approach of lung segmentation on chest CT images using graph cuts10.1016/j.neucom.2015.05.044 ↗
18. Navya, K., and Pradeep, G. (2018, January 18–19). Lung Nodule Segmentation Using Adaptive Thresholding and Watershed Transform. Proceedings of the 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India.10.1109/RTEICT42901.2018.9012577 ↗
19. Segmentation of pulmonary nodules using a GMM fuzzy C-means algorithm10.1109/ACCESS.2020.2968936 ↗
20. Savic, M., Ma, Y., Ramponi, G., Du, W., and Peng, Y. (2021). Lung nodule segmentation with a region-based fast marching method. Sensors, 21.10.3390/s21051908 ↗
21. Lung CT image segmentation using deep neural networks10.1016/j.procs.2018.01.104 ↗
22. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.10.1007/978-3-319-24574-4_28 ↗
23. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
24. Misra, D. (2019). Mish: A self regularized non-monotonic neural activation function. arXiv.
25. Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: Probing the Lung Image Database Consortium dataset with two statistical learning methods10.1117/1.JMI.3.4.044504 ↗
26. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C.L. (2014, January 6–12). Microsoft coco: Common objects in context. Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland.10.1007/978-3-319-10602-1_48 ↗
27. Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., and Guadarrama, S. (2017, January 21–26). Speed/accuracy trade-offs for modern convolutional object detectors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.351 ↗
28. Zeren, M.T., Aytulun, S.K., and Kırelli, Y. (2020). Comparison of SSD and faster R-CNN algorithms to detect the airports with data set which obtained from unmanned aerial vehicles and satellite images. Avrupa Bilim ve Teknoloji Dergisi, 643–658.10.31590/ejosat.742789 ↗
29. Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15–20). Dual attention network for scene segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00326 ↗
30. Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 21–26). Large kernel matters–improve semantic segmentation by global convolutional network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.189 ↗
31. Quader, N., Bhuiyan, M.M.I., Lu, J., Dai, P., and Li, W. (2020, January 23–28). Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks. Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK.10.1007/978-3-030-58577-8_6 ↗
32. The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans10.1118/1.3528204 ↗
33. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge10.1016/j.media.2017.06.015 ↗
34. Pytorch: An imperative style, high-performance deep learning library
35. Gulcehre, C., Sotelo, J., and Bengio, Y. (2017, January 14–19). A robust adaptive stochastic gradient method for deep learning. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.10.1109/IJCNN.2017.7965845 ↗
36. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
37. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
38. Girshick, R. (2015, January 7–13). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
39. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
40. Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21–26). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.660 ↗
41. Ma-net: A multi-scale attention network for liver and tumor segmentation10.1109/ACCESS.2020.3025372 ↗
42. Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid attention network for semantic segmentation. arXiv.
43. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
44. Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv.