2. Sch？llhuber A. Fully automatic segmentation of the myocardium in cardiac perfusion MRI. Eng Med, 2008, 14(7): 1250-1280.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
27. Hoiem D, Chodpathumwan Y, Dai Q. Diagnosing error in object detectors. Lecture Notes in Computer Science, 2012, 7574: 340-353.
[14]
8. Zheng Y, Comaniciu D. Marginal space learning for medical image analysis. New York: Springer New York, 2014.
[15]
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.
[16]
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.
[17]
11. Russakovsky O, Deng Jia, Su Hao, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis, 2015, 115(3): 211-252.
[18]
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.
[19]
13. Girshick R. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.
[20]
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.
[21]
16. Tran P V. A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint, 2016: 1-21.
[22]
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.
[23]
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.
[24]
20. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651.
[25]
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.
[26]
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.
[27]
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.
