A Clinical Guideline Driven Automated Linear Feature Extraction for
  Vestibular Schwannoma
Navodini Wijethilake Steve Connor Anna Oviedova Rebecca Burger Tom Vercauteren Jonathan Shapey
Vestibular Schwannoma is a benign brain tumour that grows from one of the
balance nerves. Patients may be treated by surgery, radiosurgery or with a
conservative "wait-and-scan" strategy. Clinicians typically use manually
extracted linear measurements to aid clinical decision making. This work aims
to automate and improve this process by using deep learning based segmentation
to extract relevant clinical features through computational algorithms. To the
best of our knowledge, our study is the first to propose an automated approach
to replicate local clinical guidelines. Our deep learning based segmentation
provided Dice-scores of 0.8124 +- 0.2343 and 0.8969 +- 0.0521 for extrameatal
and whole tumour regions respectively for T2 weighted MRI, whereas 0.8222 +-
0.2108 and 0.9049 +- 0.0646 were obtained for T1 weighted MRI. We propose a
novel algorithm to choose and extract the most appropriate maximum linear
measurement from the segmented regions based on the size of the extrameatal
portion of the tumour. Using this tool, clinicians will be provided with a
visual guide and related metrics relating to tumour progression that will
function as a clinical decision aid. In this study, we utilize 187 scans
obtained from 50 patients referred to a tertiary specialist neurosurgical
service in the United Kingdom. The measurements extracted manually by an expert
neuroradiologist indicated a significant correlation with the automated
measurements (p < 0.0001).