Abstract
Lesion detection is an essential technique in medical diagnostic systems. Since there are great differences in intensity and appearance within a same lesion category, lesion detection from computed tomography (CT) scans is still a challenging task. Sufficiently using 3D context information become the research hotpot in lesion detection area, since algorithms can benefit from geometry and texture of lesions. Motivated by this trend, we propose a multi-scale CNN based on 3D context fusion, called M3DCF, for extracting lesion area from CT scans. In order to speed up the algorithm, the one-stage regression-based detector, rather than region proposal network, is adopted. Specifically, we employ 3D context fusion strategy that allows M3DCF fusing features from neighboring slices. Finally, we use a multi-scale scheme to combine low-level and high-level features. This strategy allows us to get more meaningful semantic information. The experimental results conducted on DeepLesion dataset indicates that the proposed method outperformed state-of-the-arts, including RetinaNet, Faster R-CNN, and 3DCE. The source code is available on https://github.com/JMUAIA/M3DCF.
The first author is a student. This work is supported by the National Natural Science Foundation of China under Grant No. 61702251, the Key Technical Project of Fujian Province under Grant No. 2017H6015.
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References
Yan, K., et al.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)
Krizhevsky, A., Sutskever, I. , Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
Everingham, M., et al.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Uijlings, J.R.R., et al.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (2015)
He, K., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Dai, J., et al.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems (2016)
Sermanet, P., et al.: OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Lin, T.-Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
Redmon, J., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Fu, C.-Y., et al.: DSSD: deconvolutional single shot detector. arXiv preprint arXiv:1701.06659 (2017)
Dou, Q., Chen, H., Yu, L., et al.: Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans. Med. Imaging 35(5), 1182–1195 (2016)
Hwang, S., Kim, H.E.: Self-transfer learning for fully weakly supervised object localization. arXiv preprint arXiv:1602.01625 (2016)
Teramoto, A., Fujita, H., Yamamuro, O., et al.: Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med. phys. 43(6Part1), 2821–2827 (2016)
Yan, K., Bagheri, M., Summers, R.M.: 3D context enhanced region-based convolutional neural network for end-to-end lesion detection. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 511–519. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_58
Courbariaux, M., et al.: Binarized neural networks: training deep neural networks with weights and activations constrained to +1 or \(-\)1. arXiv preprint arXiv:1602.02830 (2016)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Ephraim, Y., Malah, D.: Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans. Acoust. Speech Signal Process. 32(6), 1109–1121 (1984)
De Boer, P.-T., et al.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)
Levinson, N.: The Wiener (root mean square) error criterion in filter design and prediction. J. Math. Phys. 25(1–4), 261–278 (1946)
Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res 30(1), 79–82 (2005)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
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Wu, Z., Chen, J., Wang, Z., Su, J., Cai, G. (2019). Multi-scale Convolutional Neural Network Based on 3D Context Fusion for Lesion Detection. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_49
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