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The overall goal of this work is to develop a rapid, accurate and fully automated software tool to
estimate patient-specific organ doses from computed tomography (CT) scans using a deterministic
Boltzmann Transport Equation solver and automated CT segmentation algorithms. This work
quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm.
The investigated algorithm uses a combination of feature-based and atlas-based methods. A multiatlas
approach was also investigated. We hypothesize that the auto-segmentation algorithm is
sufficiently accurate to provide organ dose estimates since random errors at the organ boundaries
will average out when computing the total organ dose. To test this hypothesis, twenty head-neck CT
scans were expertly segmented into nine regions. A leave-one-out validation study was performed,
where every case was automatically segmented with each of the remaining cases used as the expert
atlas, resulting in nineteen automated segmentations for each of the twenty datasets. The segmented
regions were applied to gold-standard Monte Carlo dose maps to estimate mean and peak organ
doses. The results demonstrated that the fully automated segmentation algorithm estimated the mean
organ dose to within 10% of the expert segmentation for regions other than the spinal canal, with
median error for each organ region below 2%. In the spinal canal region, the median error was 7%
across all data sets and atlases, with a maximum error of 20%. The error in peak organ dose was
below 10% for all regions, with a median error below 4% for all organ regions. The multiple-case
atlas reduced the variation in the dose estimates and additional improvements may be possible with
more robust multi-atlas approaches. Overall, the results support potential feasibility of an automated
segmentation algorithm to provide accurate organ dose estimates.
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