Automated ventricular segmentation and shunt failure detection using convolutional neural networks.

Journal: Scientific reports
Published Date:

Abstract

While ventricular shunts are the main treatment for adult hydrocephalus, shunt malfunction remains a common problem that can be challenging to diagnose. Computer vision-derived algorithms present a potential solution. We designed a feasibility study to see if such an algorithm could automatically predict ventriculomegaly indicative of shunt failure in a real-life adult hydrocephalus population. We retrospectively identified a consecutive series of adult shunted hydrocephalus patients over an eight-year period. Associated computed tomography scans were extracted and each scan was reviewed by two investigators. A machine learning algorithm was trained to identify the lateral and third ventricles, and then applied to test scans. Results were compared to human performance using Sørensen-Dice coefficients, calculated total ventricular volumes, and ventriculomegaly as documented in the electronic medical record. 5610 axial images from 191 patients were included for final analysis, with 52 segments (13.6% of total data) reserved for testing. Algorithmic performance on the test group averaged a Dice score of 0.809 ± 0.094. Calculated total ventricular volumes did not differ significantly between computer-derived volumes and volumes marked by either the first reviewer or second reviewer (p > 0.05). Algorithm detection of ventriculomegaly was correct in all test cases and this correlated with correct prediction of need for shunt revision in 92.3% of test cases. Though development challenges remain, it is feasible to create automated algorithms that detect ventriculomegaly in adult hydrocephalus shunt malfunction with high reliability and accuracy.

Authors

  • Kevin T Huang
    2Department of Neurosurgery, Harvard Medical School, Cambridge, Massachusetts.
  • Jack McNulty
    Columbia Vagelos College of Physicians and Surgeons, New York, NY, USA.
  • Helweh Hussein
    Department of Neurosurgery, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA.
  • Neil Klinger
    Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA.
  • Melissa M J Chua
    Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA.
  • Patrick R Ng
    Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA.
  • Joshua Chalif
    Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA.
  • Neel H Mehta
    Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA.
  • Omar Arnaout
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: oarnaout@bwh.harvard.edu.