A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning_Journal of Biomedical Engineering

Authors：

TAN Shuangping ¹ , LI Jun ¹ , ZHANG Xiaojuan ² , YAN Xinyue ² , ZHANG Tong ² , WU Xiali ³ , LIU Ziqiang ³ , LI Lili ³ , FENG Juan ³ , HAN Haibin ³ , TANG Guoying ³ , HAN Junzhou ³ ,  DENG Youfeng ³

1. Wuhan Puai Hospital (Wuhan Fourth Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430033, P. R. China;
2. Wuhan University, Wuhan 430079, P. R. China;
3. The First People’s Hospital of Jiangxia District Wuhan, Wuhan 430200, P. R. China;

Corresponding author：

DENG Youfeng, Email: jxyydyf@163.com

Keywords：

Pulmonary nodule detection; Active learning; Interactive review

DOI：

10.7507/1001-5515.202310044

Video：

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Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5−9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.

Citation： TAN Shuangping, LI Jun, ZHANG Xiaojuan, YAN Xinyue, ZHANG Tong, WU Xiali, LIU Ziqiang, LI Lili, FENG Juan, HAN Haibin, TANG Guoying, HAN Junzhou, DENG Youfeng. A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning. Journal of Biomedical Engineering, 2024, 41(3): 503-510. doi: 10.7507/1001-5515.202310044 Copy

1.	Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer, 2015, 136(5): E359-E386.
2.	Miller K D, Nogueira L, Mariotto A B, et al. Cancer treatment and survivorship statistics, 2019. CA: A Cancer Journal for Clinicians, 2019, 69(5): 363-385.
3.	王泽华. 肺结节AI辅助检测产品的临床应用效果及影响因素分析概述. 中国医疗设备, 2021, 36(12): 10-14.
4.	Gu Y, Chi J, Liu J, et al. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Computers in Biology and Medicine, 2021, 137: 104806.
5.	Kim H J, Lee D. Image denoising with conditional generative adversarial networks (CGAN) in low dose chest images. Nuclear Instruments and Methods in Physics Research Section A, 2020, 954: 161914.
6.	Nemoto T, Futakami N, Yagi M, et al. Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi. Journal of Radiation Research, 2020, 61(2): 257-264.
7.	Wang D, Zhang Y, Zhang K, et al. FocalMix: semi-supervised learning for 3D medical image detection//CVF Conference on Computer Vision and Pattern Recognition, Seattle: IEEE, 2020, 3951-3960.
8.	El-Regaily S A, Salem M A M, Aziz M H A, et al. Multi-view convolutional neural network for lung nodule false positive reduction. Expert Systems with Applications, 2020, 162: 113017.
9.	Sun Y, Tang J, Lei W, et al. 3D segmentation of pulmonary nodules based on multi-view and semi-supervised. IEEE Access, 2020, 8: 26457-26467.
10.	Setio A A A, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Medical Image Analysis, 2017, 42: 1-13.
11.	王海林, 冯瑞, 张晓波. 融合深度主动学习的医学图像半自动标注系统. 计算机系统应用, 2023, 32(2): 75-82.
12.	Brown M, Browning P, Wahi-Anwar M W, et al. Integration of chest CT CAD into the clinical workflow and impact on radiologist efficiency. Academic radiology, 2019, 26(5): 626-631.
13.	Settles B. Active learning literature survey. Wisconsin: University of Wisconsin-madison, 2010.
14.	Tran M, Vo-Ho V K, Le N T H. 3DConvCaps: 3DUnet with convolutional capsule encoder for medical image segmentation//2022 26th International Conference on Pattern Recognition (ICPR), Montreal: IEEE, 2022: 4392-4398.
15.	Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016): 19th International Conference, Athens: MICCAI, 2016: 424-432.
16.	Akbar A S, Fatichah C, Suciati N. Single level UNet3D with multipath residual attention block for brain tumor segmentation. Journal of King Saud University: Computer and Information Sciences, 2022, 34(6): 3247-3258.
17.	Agnes S A, Anitha J. Efficient multiscale fully convolutional UNet model for segmentation of 3D lung nodule from CT image. Journal of Medical Imaging, 2022, 9(5): 052402.
18.	Xiao Z, Liu B, Geng L, et al. Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry, 2020, 12(11): 1787.
19.	Buch N, Orwell J, Velastin S A. 3D extended histogram of oriented gradients (3DHOG) for classification of road users in urban scenes//British Machine Vision Conference, London: BMVA, 2009.

1. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer, 2015, 136(5): E359-E386.
2. Miller K D, Nogueira L, Mariotto A B, et al. Cancer treatment and survivorship statistics, 2019. CA: A Cancer Journal for Clinicians, 2019, 69(5): 363-385.
3. 王泽华. 肺结节AI辅助检测产品的临床应用效果及影响因素分析概述. 中国医疗设备, 2021, 36(12): 10-14.
4. Gu Y, Chi J, Liu J, et al. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Computers in Biology and Medicine, 2021, 137: 104806.
5. Kim H J, Lee D. Image denoising with conditional generative adversarial networks (CGAN) in low dose chest images. Nuclear Instruments and Methods in Physics Research Section A, 2020, 954: 161914.
6. Nemoto T, Futakami N, Yagi M, et al. Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi. Journal of Radiation Research, 2020, 61(2): 257-264.
7. Wang D, Zhang Y, Zhang K, et al. FocalMix: semi-supervised learning for 3D medical image detection//CVF Conference on Computer Vision and Pattern Recognition, Seattle: IEEE, 2020, 3951-3960.
8. El-Regaily S A, Salem M A M, Aziz M H A, et al. Multi-view convolutional neural network for lung nodule false positive reduction. Expert Systems with Applications, 2020, 162: 113017.
9. Sun Y, Tang J, Lei W, et al. 3D segmentation of pulmonary nodules based on multi-view and semi-supervised. IEEE Access, 2020, 8: 26457-26467.
10. Setio A A A, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Medical Image Analysis, 2017, 42: 1-13.
11. 王海林, 冯瑞, 张晓波. 融合深度主动学习的医学图像半自动标注系统. 计算机系统应用, 2023, 32(2): 75-82.
12. Brown M, Browning P, Wahi-Anwar M W, et al. Integration of chest CT CAD into the clinical workflow and impact on radiologist efficiency. Academic radiology, 2019, 26(5): 626-631.
13. Settles B. Active learning literature survey. Wisconsin: University of Wisconsin-madison, 2010.
14. Tran M, Vo-Ho V K, Le N T H. 3DConvCaps: 3DUnet with convolutional capsule encoder for medical image segmentation//2022 26th International Conference on Pattern Recognition (ICPR), Montreal: IEEE, 2022: 4392-4398.
15. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016): 19th International Conference, Athens: MICCAI, 2016: 424-432.
16. Akbar A S, Fatichah C, Suciati N. Single level UNet3D with multipath residual attention block for brain tumor segmentation. Journal of King Saud University: Computer and Information Sciences, 2022, 34(6): 3247-3258.
17. Agnes S A, Anitha J. Efficient multiscale fully convolutional UNet model for segmentation of 3D lung nodule from CT image. Journal of Medical Imaging, 2022, 9(5): 052402.
18. Xiao Z, Liu B, Geng L, et al. Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry, 2020, 12(11): 1787.
19. Buch N, Orwell J, Velastin S A. 3D extended histogram of oriented gradients (3DHOG) for classification of road users in urban scenes//British Machine Vision Conference, London: BMVA, 2009.

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