Using Bayesian Tissue Classification to Improve the Accuracy of Vestibular Schwannoma Volume and Growth Measurement
Elizabeth A. Vokurkaa,
Amit Herwadkarb,
Neil A. Thackera,
Richard T. Ramsdenc and
Alan Jacksona
a Division of Imaging Science and Biomedical Engineering, Department of Medicine, University of Manchester, England
b Department of Diagnostic Radiology, Central Manchester Healthcare NHS Trust, Manchester, England
c Department of Otolaryngology, Central Manchester Healthcare NHS Trust, Manchester, England
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FIG 1. Illustration of the basis functions for each of the tissue subsets. Gaussian curves represent each of the three tissues in voxels containing a single tissue type. Reflected triangle pairs convolved with gaussian curves represent the content of voxels containing mixtures of tissues. Three levels of shading identify the contribution of each of the three tissues in both pure and partial volume voxels.
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FIG 2. Algorithm at work.
A, Contrast-enhanced T1-weighted image of a vestibular schwannoma.
B, Histogram of the region of interest fit with the theoretical basis functions.
C, Once the fit is optimized, the relative probability that vestibular schwannoma tissue could produce each voxel’s intensity is determined from the function. This should be directly related to the proportion of the tissue in that voxel.
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FIG 3. Computer-generated phantom contains similar intensity and noise characteristics of pure and partial volume voxels of CSF, brain tissue, and tumor tissue. The artificial tumors ranged from 12 to 80 voxels in total volume. The phantom is constructed so that the absolute true volumes of the tumors are known before the effects of noise and partial volume averaging are added.
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FIG 4. A, Illustration of the problem of taking the mean diameter as an indicator of tumor size. Although there is some correlation between volume calculations and averaged diameter measurements at large volume, this correlation breaks down at small volumes.
B, Expansion of the plot of manual volume estimation versus Bayesian partial volume segmentation for smaller tumors shows good linear correlation with manual volume estimates at all tumor sizes.
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FIG 5. Example of follow-up study of vestibular schwannoma, illustrating a small tumor with no evidence of growth.
A, Initial image of a 0.8-cm vestibular schwannoma.
B, Magnified view of initial image.
C, Follow-up image obtained after 11 months.
D, Probability map of initial tumor location.
E, Probability map of final tumor location.
F, Subtraction map shows differences of more than +25% (open squares) and less than -25% (shaded squares) in voxel volume change after subtraction of the two probability maps shown in D and E. This shows that the tumor has moved in the second image relative to the first, that all differences are due to partial volume changes, and that the number of voxels with apparent volume loss is matched by the number with volume gain. Overall difference in volume of the whole tumor is one voxel, well within the accuracy of the measurement.
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FIG 6. Example of follow-up study of vestibular schwannoma, illustrating a tumor with significant growth.
A, Initial image of a 2-cm vestibular schwannoma.
B, Magnified view of the initial image.
C, Follow-up image obtained after 7 months.
D, Tumor probability map of initial maximal diameter.
E, Tumor probability map of final maximal diameter.
F, Subtraction map of the two probability images shows voxels with a volume change of more than 50%. All voxels that show change show an increase in tumor volume. There is no evidence of significant misregistration between the images, and the overall volume increase is 38%.
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