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Validation of clinical acceptability of an atlas-based segmentation
algorithm for the delineation of organs at risk in head and neck
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The aim of this study was to assess whether clinically acceptable segmentations of
organs at risk (OARs) in head and neck cancer can be obtained automatically and
efficiently using the novel “similarity and truth estimation for propagated
segmentations” (STEPS) compared to the traditional “simultaneous truth and
performance level estimation” (STAPLE) algorithm.
First, 6 OARs were contoured by 2 radiation oncologists in a
dataset of 100 patients with head and neck cancer on planning
images. Each image in the dataset was then automatically
segmented with STAPLE and STEPS using those manual contours. Dice similarity
coefficient (DSC) was then used to compare the accuracy of these automatic
methods. Second, in a blind experiment, three separate and distinct trained
physicians graded manual and automatic segmentations into one of the following
three grades: clinically acceptable as determined by universal delineation
guidelines (grade A), reasonably acceptable for clinical practice upon manual
editing (grade B), and not acceptable (grade C). Finally, STEPS segmentations
graded B were selected and one of the physicians manually edited them to grade A.
Editing time was recorded.
Significant improvements in DSC can be seen when using the STEPS algorithm on
large structures such as the brainstem, spinal canal, and left/right parotid
compared to the STAPLE algorithm (all p < 0.001). In addition,
across all three trained physicians, manual and STEPS segmentation grades were not
significantly different for the brainstem, spinal canal, parotid (right/left), and
chiasm (all p > 0.100). In contrast, STEPS
segmentation grades were lower for the eyes (p < 0.001). Across all
OARs and all physicians, STEPS produced segmentations graded as well as manual
contouring at a rate of 83%, giving a lower bound on this rate of 80% with 95%
confidence. Reduction in manual interaction time was on average 61% and 93% when
automatic segmentations did and did not, respectively, require manual editing.
The STEPS algorithm showed better performance than the STAPLE algorithm in
segmenting OARs for radiotherapy of the head and neck. It can automatically
produce clinically acceptable segmentation of OARs, with results as relevant as
manual contouring for the brainstem, spinal canal, the parotids (left/right), and
chiasm. A substantial reduction in manual labor was achieved when using STEPS even
when manual editing was necessary.
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