Multi-scale Attentional Network for Multi-focal Segmentation of Active Bleed After Pelvic Fractures

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11861)


Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality. Automated segmentation of multiple foci of arterial bleeding from abdominopelvic trauma CT could provide rapid objective measurements of the total extent of active bleeding, potentially augmenting outcome prediction at the point of care, while improving patient triage, allocation of appropriate resources, and time to definitive intervention. In spite of the importance of active bleeding in the quick tempo of trauma care, the task is still quite challenging due to the variable contrast, intensity, location, size, shape, and multiplicity of bleeding foci. Existing work presents a heuristic rule-based segmentation technique which requires multiple stages and cannot be efficiently optimized end-to-end. To this end, we present, Multi-Scale Attentional Network (MSAN), the first yet reliable end-to-end network, for automated segmentation of active hemorrhage from contrast-enhanced trauma CT scans. MSAN consists of the following components: (1) an encoder which fully integrates the global contextual information from holistic 2D slices; (2) a multi-scale strategy applied both in the training stage and the inference stage to handle the challenges induced by variation of target sizes; (3) an attentional module to further refine the deep features, leading to better segmentation quality; and (4) a multi-view mechanism to leverage the 3D information. MSAN reports a significant improvement of more than \(7\%\) compared to prior arts in terms of DSC.



This work was supported by NIBIB (National Institute of Biomedical Imaging and Bioengineering)/NIH under award number K08EB027141, University of Maryland Institute for Clinical and Translational Research Accelerated Translational Incubator Pilot (ATIP) award and Radiologic Society of North America (RSNA) Research Scholar Award #1605.


  1. 1.
    Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: International Conference on Learning Representations (2015)Google 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). Scholar
  3. 3.
    Cullinane, D.C., et al.: Eastern association for the surgery of trauma practice management guidelines for hemorrhage in pelvic fracture-update and systematic review. J. Trauma Acute Care Surg. 71(6), 1850–1868 (2011)CrossRefGoogle Scholar
  4. 4.
    Davuluri, P., et al.: Hemorrhage detection and segmentation in traumatic pelvicinjuries. Comput. Math. Methods Med. 2012(2012)CrossRefGoogle Scholar
  5. 5.
    Dreizin, D., et al.: CT prediction model for major arterial injury after blunt pelvic ring disruption. Radiology 287(3), 1061–1069 (2018)CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)CrossRefGoogle Scholar
  8. 8.
    Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. arXiv (2016)Google Scholar
  9. 9.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)Google Scholar
  10. 10.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  11. 11.
    Roth, H.R., Lu, L., Farag, A., Sohn, A., Summers, R.M.: Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 451–459. Springer, Cham (2016). Scholar
  12. 12.
    Sathy, A.K., et al.: The effect of pelvic fracture on mortality after trauma: an analysis of 63,000 trauma patients. JBJS 91(12), 2803–2810 (2009)CrossRefGoogle Scholar
  13. 13.
    Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR (2018)Google Scholar
  14. 14.
    Yu, Q., Xie, L., Wang, Y., Zhou, Y., Fishman, E.K., Yuille, A.L.: Recurrent saliency transformation network: incorporating multi-stage visual cues for small organ segmentation. In: CVPR, pp. 8280–8289 (2018)Google Scholar
  15. 15.
    Zhou, Y., Xie, L., Shen, W., Wang, Y., Fishman, E.K., Yuille, A.L.: A fixed-point model for pancreas segmentation in abdominal CT scans. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 693–701. Springer, Cham (2017). Scholar
  16. 16.
    Zhu, W., et al.: AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med. Phys. 46(2), 576–589 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The Johns Hopkins UniversityBaltimoreUSA
  2. 2.University of Maryland & R. Adams Cowley Shock Trauma CenterBaltimoreUSA

Personalised recommendations