Contextual Deep Regression Network for Volume Estimation in Orbital CT

  • Shikha ChagantiEmail author
  • Cam Bermudez
  • Louise A. Mawn
  • Thomas Lasko
  • Bennett A. Landman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


Diseases of the optic nerve cause structural changes observable through clinical computed tomography (CT) imaging. Previous work has shown that multi-atlas methods can be used to segment and extract volumetric measurements from the optic nerve, which are associated with visual disability and disease. In this work, we trained a weakly supervised convolutional neural network to learn optic nerve volumes directly, without segmentation. Furthermore, we explored the role of contextual electronic medical record (EMR) information, specifically ICD-9 codes, to improve optic nerve volume estimation. We constructed a merged network to combine data from imaging as well as EMR and demonstrated that context improved volume prediction, with a 15% increase in explained-variance \( \left( {R^{2} } \right) \). Finally, we compared disease prediction models using volumes learned from multi-atlas, CNN, and contextual-CNN. We observed that the predicted optic nerve volume from merge-CNN had an AUC of 0.74 for classification of disease, as compared to an AUC of 0.54 using the multi-atlas metric. This is the first work to show that a contextually derived volume biomarker is more accurate than volume estimations through multi-atlas or weakly supervised image CNN. These results highlight the potential for image processing improvements by incorporating non-imaging data.


Weak supervision Optic nerve CT Segmentation-free EMR Volume estimation Non-imaging data 



This research was supported by NSF CAREER 1452485 and NIH grants 5R21EY024036. This research was conducted with the support from Intramural Research Program, National Institute on Aging, NIH. This study was conducted in part using the resources of the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville, TN. In addition, this project was supported in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01, and is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06. Finally, this work was also supported by the National Institutes of Health in part by the National Institute of Biomedical Imaging and Bioengineering training grant T32-EB021937.


  1. 1.
    Becker, M., Masterson, K., Delavelle, J., Viallon, M., Vargas, M.-I., Becker, C.D.: Imaging of the optic nerve. Eur. J. Radiol. 74(2), 299–313 (2010)CrossRefGoogle Scholar
  2. 2.
    Yao, X., et al.: Structural-functional relationships between eye orbital imaging biomarkers and clinical visual assessments. In: SPIE Medical Imaging, p. 101331F (2017)Google Scholar
  3. 3.
    Chaganti, S., Robinson, J.R., Bermudez, C., Lasko, T., Mawn, L.A., Landman, B.A.: EMR-radiological phenotypes in diseases of the optic nerve and their association with visual function. In: Cardoso, M.Jorge, Arbel, T., Carneiro, G., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 373–381. Springer, Cham (2017). Scholar
  4. 4.
    Chaganti, S., et al.: Electronic medical record context signatures improve diagnostic classification using medical image computing. IEEE J. Biomed. Heal. Informatics PP, 1 (2018)Google Scholar
  5. 5.
    Ball, A.K., Clarke, C.E.: Idiopathic intracranial hypertension. Lancet Neurol. 5(5), 433–442 (2006)CrossRefGoogle Scholar
  6. 6.
    Binder, D.K., Horton, J.C., Lawton, M.T., McDermott, M.W.: Idiopathic intracranial hypertension. Neurosurgery 54(3), 538–552 (2004)CrossRefGoogle Scholar
  7. 7.
    Nunery, W.R., Nunery, C.W., Martin, R.T., Truong, T.V., Osborn, D.R.: The risk of diplopia following orbital floor and medial wall decompression in subtypes of ophthalmic Graves’ disease. Ophthalmic Plast. Reconstr. Surg. 13(3), 153–160 (1997)CrossRefGoogle Scholar
  8. 8.
    Laurberg, P., Berman, D.C., Bülow Pedersen, I., Andersen, S., Carlé, A.: Double vision is a major manifestation in moderate to severe graves’ orbitopathy, but it correlates negatively with inflammatory signs and proptosis. J. Clin. Endocrinol. Metab. 100(5), 2098–2105 (2015)CrossRefGoogle Scholar
  9. 9.
    Ophthalmopathy, G.: Graves’ Ophthalmopathy (2010)Google Scholar
  10. 10.
    Harrigan, R.L., et al.: Robust optic nerve segmentation on clinically acquired computed tomography. J. Med. Imaging 1(3), 34006 (2014)CrossRefGoogle Scholar
  11. 11.
    Hussain, M.A., Amir-Khalili, A., Hamarneh, G., Abugharbieh, R.: Segmentation-free kidney localization and volume estimation using aggregated orthogonal decision CNNs. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.Louis, Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 612–620. Springer, Cham (2017). Scholar
  12. 12.
    Luo, G., Dong, S., Wang, K., Zuo, W., Cao, S., Zhang, H.: Multi-views fusion CNN for left ventricular volumes estimation on cardiac MR images. IEEE Trans. Biomed. Eng. 65(9), 1924–1934 (2018)CrossRefGoogle Scholar
  13. 13.
    Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imaging 32(7), 1239–1248 (2013)CrossRefGoogle Scholar
  14. 14.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  15. 15.
    Asman, A.J., Landman, B.A.: Non-local statistical label fusion for multi-atlas segmentation. Med. Image Anal. 17(2), 194–208 (2013)CrossRefGoogle Scholar
  16. 16.
    Denny, J.C., et al.: PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics 26(9), 1205–1210 (2010)CrossRefGoogle Scholar
  17. 17.
    Chaganti, S., Nabar, K., Landman, B.: pyPheWAS (2017).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shikha Chaganti
    • 1
    Email author
  • Cam Bermudez
    • 2
  • Louise A. Mawn
    • 3
  • Thomas Lasko
    • 4
  • Bennett A. Landman
    • 5
  1. 1.Computer ScienceVanderbilt UniversityNashvilleUSA
  2. 2.Biomedical EngineeringVanderbilt UniversityNashvilleUSA
  3. 3.Department of OphthalmologyVanderbilt University Medical CenterNashvilleUSA
  4. 4.Biomedical InformaticsVanderbilt University Medical CenterNashvilleUSA
  5. 5.Electrical EngineeringVanderbilt UniversityNashvilleUSA

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