Medical computer-aided detection method based on deep learning_Journal of Biomedical Engineering

Authors：

 TAO Pan ^1,2 , FU Zhongliang ¹ , ZHU Kai ^1,2 , WANG Lili ^1,2

1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, P.R.China;
2. University of Chinese Academy of Sciences, Beijing 100049, P.R.China;

Corresponding author：

TAO Pan, Email: 284792640@qq.com

Keywords：

computer-aided detection; magnetic resonance image; echocardiogram; object detection; region convolutional neural network

DOI：

10.7507/1001-5515.201611064

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.

Citation： TAO Pan, FU Zhongliang, ZHU Kai, WANG Lili. Medical computer-aided detection method based on deep learning. Journal of Biomedical Engineering, 2018, 35(3): 368-375. doi: 10.7507/1001-5515.201611064 Copy

1.	Cheng J Z, Ni D, Chou Y H, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6: 24454.
2.	Schöllhuber A. Fully automatic segmentation of the myocardium in cardiac perfusion MRI. Eng Med, 2008, 14(7): 1250-1280.
3.	Lu Y, Radau P, Connelly K, et al. Segmentation of left ventricle in cardiac cine MRI: an automatic image-driven method//Ayache N, Delingette H, Sermesant M. Functional Imaging and Modeling of the Heart. FIMH 2009. Lecture Notes in Computer Science, vol 5528. Berlin, Heidelberg: Springer, 5528: 339-347.
4.	Petitjean C, Dacher J N. A review of segmentation methods in short axis cardiac MR images. Med Image Anal, 2011, 15(2): 169-184.
5.	Kellman P, Lu Xiaoguang, Jolly M P, et al. Automatic LV localization and view planning for cardiac MRI acquisition. Journal of Cardiovascular Magnetic Resonance, 2011, 13(S1): 33-39.
6.	Zhou S K, Georgescu B, Zhou X S, et al. Image based regression using boosting method//Proceedings of the IEEE International Conference on Computer Vision. Beijing: IEEE, 2005: 541-548.
7.	She Y, Liu D C. An interactive editing tool from arbitrary slices in 3D ultrasound volume data//Proceedings of the World Congress on Medical Physics and Biomedical Engineering. Berlin, Heidelberg: Springer, 2007, 14: 2447-2451.
8.	Zheng Y, Comaniciu D. Marginal space learning for medical image analysis. New York: Springer New York, 2014.
9.	Razavian A S, Azizpour H, Sullivan J, et al. CNN features off-the-shelf: An astounding baseline for recognition//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Columbus: IEEE, 2014: 512-519.
10.	Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.
11.	Russakovsky O, Deng Jia, Su Hao, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis, 2015, 115(3): 211-252.
12.	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks//Proceedings of the Advances in Neural Information Processing Systems 25. New York: Curran Associates, Inc, 2012, 60(2): 1-9.
13.	Girshick R. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.
14.	Akram S U, Kannala J, Eklund L, et al. Cell segmentation proposal network for microscopy image analysis. Deep Learning and Data Labeling for Medical Applications, 2016: 21-29.
15.	Emad O, Yassine I A, Fahmy A S. Automatic localization of the left ventricle in cardiac MRI images using deep learning//Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan: IEEE, 2015: 683-686.
16.	Tran P V. A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint, 2016: 1-21.
17.	Chen H, Zheng Y, Park J H, et al. Iterative multi-domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images. Medical Image Computing and Computer-Assisted Intervention, 2016: 487-495.
18.	Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks//Proceedings of the Advances in Neural Information Processing Systems 25. Montreal: Curran Associates, Inc, 2015: 1021-1029.
19.	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2015, 37(9): 1904-1916.
20.	Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651.
21.	Jaderberg M, Simonyan K, Zisserman A, et al. Spatial transformer networks//Advances in Neural Information Processing Systems 28: 29th Annual Conference on Neural Information Processing Systems 2015. Montreal: Curran Associates, Inc, 2015: 2017-2025.
22.	Beyer L, Hermans A, Leibe B. Biternion nets: continuous head pose regression from discrete training labels. Lecture Notes in Computer Science, 2015, 9358: 157-168.
23.	Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1063-1069.
24.	Andreopoulos A, Tsotsos J K. Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Med Image Anal, 2008, 12(3): 335-357.
25.	Chatfield K, Simonyan K, Vedaldi A, et al. Return of the devil in the details: delving deep into convolutional nets//Proceedings of the British Machine Vision Conference. Lodon: BMVC, 2014: 1-11.
26.	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2015, 7(3): 171-180.
27.	Hoiem D, Chodpathumwan Y, Dai Q. Diagnosing error in object detectors. Lecture Notes in Computer Science, 2012, 7574: 340-353.

1. Cheng J Z, Ni D, Chou Y H, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6: 24454.
2. Schöllhuber A. Fully automatic segmentation of the myocardium in cardiac perfusion MRI. Eng Med, 2008, 14(7): 1250-1280.
3. Lu Y, Radau P, Connelly K, et al. Segmentation of left ventricle in cardiac cine MRI: an automatic image-driven method//Ayache N, Delingette H, Sermesant M. Functional Imaging and Modeling of the Heart. FIMH 2009. Lecture Notes in Computer Science, vol 5528. Berlin, Heidelberg: Springer, 5528: 339-347.
4. Petitjean C, Dacher J N. A review of segmentation methods in short axis cardiac MR images. Med Image Anal, 2011, 15(2): 169-184.
5. Kellman P, Lu Xiaoguang, Jolly M P, et al. Automatic LV localization and view planning for cardiac MRI acquisition. Journal of Cardiovascular Magnetic Resonance, 2011, 13(S1): 33-39.
6. Zhou S K, Georgescu B, Zhou X S, et al. Image based regression using boosting method//Proceedings of the IEEE International Conference on Computer Vision. Beijing: IEEE, 2005: 541-548.
7. She Y, Liu D C. An interactive editing tool from arbitrary slices in 3D ultrasound volume data//Proceedings of the World Congress on Medical Physics and Biomedical Engineering. Berlin, Heidelberg: Springer, 2007, 14: 2447-2451.
8. Zheng Y, Comaniciu D. Marginal space learning for medical image analysis. New York: Springer New York, 2014.
9. Razavian A S, Azizpour H, Sullivan J, et al. CNN features off-the-shelf: An astounding baseline for recognition//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Columbus: IEEE, 2014: 512-519.
10. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.
11. Russakovsky O, Deng Jia, Su Hao, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis, 2015, 115(3): 211-252.
12. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks//Proceedings of the Advances in Neural Information Processing Systems 25. New York: Curran Associates, Inc, 2012, 60(2): 1-9.
13. Girshick R. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.
14. Akram S U, Kannala J, Eklund L, et al. Cell segmentation proposal network for microscopy image analysis. Deep Learning and Data Labeling for Medical Applications, 2016: 21-29.
15. Emad O, Yassine I A, Fahmy A S. Automatic localization of the left ventricle in cardiac MRI images using deep learning//Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan: IEEE, 2015: 683-686.
16. Tran P V. A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint, 2016: 1-21.
17. Chen H, Zheng Y, Park J H, et al. Iterative multi-domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images. Medical Image Computing and Computer-Assisted Intervention, 2016: 487-495.
18. Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks//Proceedings of the Advances in Neural Information Processing Systems 25. Montreal: Curran Associates, Inc, 2015: 1021-1029.
19. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2015, 37(9): 1904-1916.
20. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651.
21. Jaderberg M, Simonyan K, Zisserman A, et al. Spatial transformer networks//Advances in Neural Information Processing Systems 28: 29th Annual Conference on Neural Information Processing Systems 2015. Montreal: Curran Associates, Inc, 2015: 2017-2025.
22. Beyer L, Hermans A, Leibe B. Biternion nets: continuous head pose regression from discrete training labels. Lecture Notes in Computer Science, 2015, 9358: 157-168.
23. Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1063-1069.
24. Andreopoulos A, Tsotsos J K. Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Med Image Anal, 2008, 12(3): 335-357.
25. Chatfield K, Simonyan K, Vedaldi A, et al. Return of the devil in the details: delving deep into convolutional nets//Proceedings of the British Machine Vision Conference. Lodon: BMVC, 2014: 1-11.
26. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2015, 7(3): 171-180.
27. Hoiem D, Chodpathumwan Y, Dai Q. Diagnosing error in object detectors. Lecture Notes in Computer Science, 2012, 7574: 340-353.

Journal of Biomedical Engineering

Medical computer-aided detection method based on deep learning

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content