Abstract
Detecting different kinds of lesions in computed tomography (CT) scans at the same time is a difficult but important task for a computer-aided diagnosis (CADx) system. Compared to single-lesion detection methods, our lesion detection method considers additional intra-class differences. In this work, we present a CT image analysis framework for lesion detection. Our model is developed based on a dense region-based fully convolutional network (Dense R-FCN) model using 3D context and is equipped with a dense auxiliary loss (DAL) scheme for end-to-end learning. It fuses shallow, medium, and deep features to meet the needs of detecting lesions of various sizes. Owing to its fully-connected structure, it is called Dense R-FCN. Meanwhile, the DAL supervises the intermediate hidden layers in order to maximize the use of the shallow layer information, which benefits the detection results, especially for small lesions. Experiment results on the DeepLesion dataset corroborate the efficacy of our method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bell, S., Lawrence Zitnick, C., Bala, K., et al.: Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2874–2883 (2016)
Chen, T., Li, M., Li, Y., et al.: MXNet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015)
Dai, J., Li, Y., He, K., et al.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379–387 (2016)
Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Dou, Q., Chen, H., Jin, Y., Lin, H., Qin, J., Heng, P.-A.: Automated pulmonary nodule detection via 3D convnets with online sample filtering and hybrid-loss residual learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 630–638. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_72
Kong, T., Yao, A., Chen, Y., et al.: HyperNet: towards accurate region proposal generation and joint object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 845–853 (2016)
Lee, C.Y., Xie, S., Gallagher, P., et al.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562–570 (2015)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
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
Yan, K., Wang, X., Lu, L., et al.: Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)
Zhang, R., Zhang, H., Chung, A.C.S.: A unified mammogram analysis method via hybrid deep supervision. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 107–115. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_12
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, H., Chung, A.C.S. (2019). Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-32692-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32691-3
Online ISBN: 978-3-030-32692-0
eBook Packages: Computer ScienceComputer Science (R0)
