Abstract
Stereotactic radio surgery (SRS) is the preferred treatment for brain metastases (BM), in which the delineation of metastatic lesions is one of the critical steps. Taking into consideration that the BM always have clear boundary with surrounding tissues but very small volume, the difficulty of delineation is object detection instead of segmentation. In this paper, we presented a novel lesion detection framework, called Multi-scale and Multi-level Single Shot Detector (MMSSD), to detect the BM target accurately and effectively. In MMSSD, we took advantage of multi-scale feature maps, while paid more attention on the shallow layers for small objects. Specifically, first we only preserved the applicable large-and-middle-scale features in SSD, then generated new feature representations by multi-level feature fusion module, and finally made predictions on those feature maps. The proposed MMSSD framework was evaluated on the clinical dataset, and the experiment results demonstrated that our method outperformed existing popular detectors for BM detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Patchell, R.A.: The management of brain metastases. Cancer Treat. Rev. 29(6), 533–540 (2003)
Landoni, V., Pinzi, V., Gomellini, S., et al.: 1539 poster tumor control probability in stereotactic radio-surgery for brain metastases. Radiother. Oncol. 99(1), 572 (2011)
James, A.S.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (1999)
Li, C., Xu, C., Gui, C., et al.: Level set evolution without re-initialization: a new variational formulation. In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 430–436. CVPR, Washington (2005)
Arakeri, M.P., Ram Mohana Reddy, G.: Efficient fuzzy clustering based approach to brain tumor segmentation on MR images. In: Das, V.V., Thankachan, N. (eds.) CIIT 2011. CCIS, vol. 250, pp. 790–795. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25734-6_141
Rastgarpour, M., Shanbehzadeh, J.: A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity in homogeneity. Comput. Math. Methods Med. 1, 231–239 (2014)
Kamnitsas, K., Ledig, C., Newcombe, V.F.J., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)
Liu, Y., Stojadinovic, S., Hrycushko, B., et al.: A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS ONE 12(10), 1–7 (2017)
Charron, O., Lallement, A., Jarnet, D., et al.: Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput. Biol. Med. 95, 43–54 (2018)
Grøvik, E., Yi, D., Iv, M., et al.: Deep learning enables automatic detection and segmentation of brain metastases on multi-sequence MRI. J. Magn. Reson. Imaging 3, 1–9 (2019)
Lecun, Y., Boser, B., Denker, J.S., et al.: Back-propagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (2014)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: 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
Cui, L., Ma, R., Lv, P., et al.: MDSSD: multi-scale deconvolutional single shot detector for small objects. arXiv preprint arXiv: 1805.07009 (2018)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Xu, X., Zhou, F., Liu, B., et al.: Efficient multiple organ localization in CT image using 3D region proposal network. IEEE Trans. Med. Imaging 38(8), 1885–1898 (2019)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778. CVPR, Washington (2016)
Huang, G., Liu, Z., Laurens, V.D.M., et al.: Densely connected convolutional networks. In: Proceedings of 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–9. CVPR, Washington (2016)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv: 1804.02767 (2018)
Duan, K., Bai, S., Xie, L., et al.: CenterNet: keypoint triplets for object detection. In: Proceedings of 2019 IEEE Computer Society Conference on Computer Vision and Pat-tern Recognition, p. 10. CVPR, Long Beach (2019)
De, V.B., Wolterink, J., De Jong, P., et al.: ConvNet-based localization of anatomical structures in 3D medical image. IEEE Trans. Med. Imaging 36(7), 1470–1481 (2017)
Acknowledgments
Publications of this article were sponsored by the National Science Foundation of China under Grant 61902264 and by the Key Research and Development projects in Sichuan Province under Grant 2019YFS0125.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, H., Xia, W., Liu, Y., Gu, X., Zhou, J., Zhang, Y. (2021). MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-72084-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72083-4
Online ISBN: 978-3-030-72084-1
eBook Packages: Computer ScienceComputer Science (R0)