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Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11850)

Abstract

Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. In this work, we present a deep learning approach for automated detection and segmentation of brain metastases using multimodal imaging and ensemble neural networks. In order to address small and multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This work surpasses current benchmark levels and demonstrates a reliable AI-assisted system for SRS treatment planning for multiple brain metastases.

Keywords

Brain metastases Radiosurgery Deep learning 

References

  1. 1.
    Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J.B., Meyer, P.: Automatic detection and segmentation of brain metastases on multimodal mr images with a deep convolutional neural network. Comput. Biol. Med. 95, 43–54 (2018)CrossRefGoogle Scholar
  2. 2.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_49CrossRefGoogle Scholar
  3. 3.
    Hartgerink, D.E., et al.: Stereotactic radiosurgery in the management of patients with brain metastases of non-small cell lung cancer; indications, decision tools and future directions. Front. Oncol. 8, 154 (2018)CrossRefGoogle Scholar
  4. 4.
    Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected crf for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRefGoogle Scholar
  5. 5.
    Lin, X., DeAngelis, L.M.: Treatment of brain metastases. J. Clin. Oncol. 33(30), 3475 (2015)CrossRefGoogle Scholar
  6. 6.
    Liu, Y., et al.: A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PloS One 12(10), e0185844 (2017)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  8. 8.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)Google Scholar
  9. 9.
    Pereira, G.C., Traughber, M., Muzic, R.F.: The role of imaging in radiation therapy planning: past, present, and future. BioMed Res. Int. 2014 (2014)Google Scholar
  10. 10.
    Tsao, M.N., et al.: Radiotherapeutic and surgical management for newly diagnosed brain metastasis (es): an American society for radiation oncology evidence-based guideline. Pract. Radiat. Oncol. 2(3), 210–225 (2012)CrossRefGoogle Scholar
  11. 11.
    Vinod, S.K., Jameson, M.G., Min, M., Holloway, L.C.: Uncertainties in volume delineation in radiation oncology: a systematic review and recommendations for future studies. Radiother. Oncol. 121(2), 169–179 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Massachusetts General HospitalBostonUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Department of OncologyNational Taiwan University HospitalTaipeiTaiwan
  4. 4.Vysioneer Inc.CambridgeUSA
  5. 5.Department of SurgeryNational Taiwan University HospitalTaipeiTaiwan

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