Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume

  • Trevor J. HuffEmail author
  • Parker E. Ludwig
  • David Salazar
  • Justin A. Cramer
Original Article



Hydrocephalus is a clinically significant condition which can have devastating consequences if left untreated. Currently available methods for quantifying this condition using CT imaging are unreliable and prone to error. The purpose of this study is to investigate the clinical utility of using convolutional neural networks to calculate ventricular volume and explore limitations.


A two-dimensional convolutional neural network was designed to perform fully automated ventricular segmentation on CT images. A total of 300 head CTs were collected and used in this exploration. Two hundred were used to train the network, 50 were used for validation, and 50 were used for testing.


Dice scores for the left lateral, right lateral, and third ventricle segmentations were 0.92, 0.92, and 0.79, respectively; the coefficients of determination were r2 = 0.991, r2 = 0.994, and r2 = 0.976; the average volume differences between manual and automated segmentation were 0.821 ml, 0.587 ml, and 0.099 ml.


Two-dimensional convolutional neural network architectures can be used to accurately segment and quantify intracranial ventricle volume. While further refinements are necessary, it is likely these networks could be used as a clinical tool to quantify hydrocephalus accurately and efficiently.


U-Net Convolutional neural network (CNN) Machine learning Intracranial ventricle volume Automated segmentation 



The author(s) received no financial support for the research, authorship, and/or publication of this article.

Compliance with ethical standards

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Ethical approval

For this type of study, formal consent is not required.


  1. 1.
    Smith ER, Amin-Hanjani S (2010) Evaluation and management of elevated intracranial pressure in adults. UpToDate Walth MA UpToDate Retrieved MarchGoogle Scholar
  2. 2.
    American College of Radiology ACR Appropriateness CriteriaGoogle Scholar
  3. 3.
    Aukland SM, Odberg MD, Gunny R, Chong WKK, Eide GE, Rosendahl K (2008) Assessing ventricular size: is subjective evaluation accurate enough? New MRI-based normative standards for 19-year-olds. Neuroradiology 50:1005–1011. CrossRefPubMedGoogle Scholar
  4. 4.
    Reinard K, Basheer A, Phillips S, Snyder A, Agarwal A, Jafari-Khouzani K, Soltanian-Zadeh H, Schultz L, Aho T, Schwalb JM (2015) Simple and reproducible linear measurements to determine ventricular enlargement in adults. Surg Neurol Int 6:59. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Toma AK, Holl E, Kitchen ND, Watkins LD (2011) Evans’ index revisited: the need for an alternative in normal pressure hydrocephalus. Neurosurgery 68:939–944. CrossRefPubMedGoogle Scholar
  6. 6.
    Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Chen W, Smith R, Ji S-Y, Ward KR, Najarian K (2009) Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching. BMC Med Inform Decis Mak 9(Suppl 1):S4. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Qian X, Lin Y, Zhao Y, Yue X, Lu B, Wang J (2017) Objective ventricle segmentation in brain ct with ischemic stroke based on anatomical knowledge. BioMed Res. Int. Accessed 11 Oct 2018
  9. 9.
    Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP (2018) Deep learning in neuroradiology. Am J Neuroradiol 39:1776–1784. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, White RD (2017) Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285:923–931. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, Chen J, Abcede H, Shafie M, Sugrue L, Filippi CG, Su M-Y, Yu W, Hess C, Chow D (2018) Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. AJNR Am J Neuroradiol 39:1609–1616. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Ibragimov B, Xing L (2017) Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys 44:547–557. CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Ren X, Xiang L, Nie D, Shao Y, Zhang H, Shen D, Wang Q (2018) Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images. Med Phys 45:2063–2075. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Zhao C, Carass A, Lee J, He Y, Prince JL (2017) Whole brain segmentation and labeling from CT using synthetic MR images. In: Wang Q, Shi Y, Suk H-I, Suzuki K (eds) Machine learning in medical imaging. Springer, Berlin, pp 291–298CrossRefGoogle Scholar
  15. 15.
    Cherukuri V, Ssenyonga P, Warf BC, Kulkarni AV, Monga V, Schiff SJ (2018) Learning based segmentation of CT brain images: application to postoperative hydrocephalic scans. IEEE Trans Biomed Eng 65:1871–1884. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Han M, Quon J, Kim L, Shpanskaya K, Lee E, Kestle J, Lober R, Taylor M, Ramaswamy V, Edwards M et al (2019) One hundred years of innovation: automatic detection of brain ventricular volume using deep learning in a large-scale multi-institutional study (P5. 6-022). AAN EnterprisesGoogle Scholar
  17. 17.
    D Slicer. Accessed 9 Sep 2018
  18. 18.
    Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention—MICCAI 2015. Springer, Cham, pp 234–241Google Scholar
  19. 19.
    Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. arXiv:1606.06650
  20. 20.
    He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: 2017 IEEE international conference on computer vision (ICCV), pp 2980–2988Google Scholar
  21. 21.
    Girshick R (2015) Fast R-CNN, pp 1440–1448Google Scholar
  22. 22.
    Milletari F, Ahmadi S-A, Kroll C, Plate A, Rozanski V, Maiostre J, Levin J, Dietrich O, Ertl-Wagner B, Bötzel K, Navab N (2017) Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput Vis Image Underst 164:92–102. CrossRefGoogle Scholar
  23. 23.
    Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P-M, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chen H, Dou Q, Yu L, Heng P-A (2016) VoxResNet: deep voxelwise residual networks for volumetric brain segmentationGoogle Scholar
  25. 25.
    Fritscher K, Raudaschl P, Zaffino P, Spadea MF, Sharp GC, Schubert R (2016) Deep neural networks for fast segmentation of 3D medical images. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical image computing and computer-assisted intervention—MICCAI 2016. Springer, Berlin, pp 158–165CrossRefGoogle Scholar
  26. 26.
    Kayalibay B, Jensen G, van der Smagt P (2017) CNN-based segmentation of medical imaging dataGoogle Scholar
  27. 27.
    Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJNL, Išgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35:1252–1261. CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Reubelt D, Small LC, Hoffmann MHK, Kapapa T, Schmitz BL (2009) MR imaging and quantification of the movement of the lamina terminalis depending on the CSF dynamics. Am J Neuroradiol 30:199–202. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Creighton University School of MedicineOmahaUSA
  2. 2.Department of BiomechanicsUniversity of Nebraska at OmahaOmahaUSA
  3. 3.University of Nebraska Medical CenterOmahaUSA

Personalised recommendations