Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer.
OBJECTIVE
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.


METHODS
First, 6 OARs were contoured by 2 radiation oncologists in a dataset of 100 patients with head and neck cancer on planning computed tomography 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.


RESULTS
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 optic 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.


CONCLUSIONS
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 optic chiasm. A substantial reduction in manual labor was achieved when using STEPS even when manual editing was necessary.
Center for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom.
Albert K
AK
Hoang Duc
Radiotherapy Department, University College London Hospitals, 235 Euston Road, London NW1 2BU, United Kingdom.
Gemma
G
Eminowicz
Radiotherapy Department, University College London Hospitals, 235 Euston Road, London NW1 2BU, United Kingdom.
Ruheena
R
Mendes
Radiotherapy Department, University College London Hospitals, 235 Euston Road, London NW1 2BU, United Kingdom.
Swee-Ling
SL
Wong
Center for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom.
Jamie
J
McClelland
Center for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom.
Marc
M
Modat
Center for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom.
M Jorge
MJ
Cardoso
Center for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom.
Alex F
AF
Mendelson
Department of Medical Physics and Bioengineering, University College London, London WC1E 6BT, United Kingdom.
Catarina
C
Veiga
Mirada Medical UK, Oxford Center for Innovation, New Road, Oxford OX1 1BY, United Kingdom.
Timor
T
Kadir
Radiotherapy Department, University College London Hospitals, 235 Euston Road, London NW1 2BU, United Kingdom.
Derek
D
D'Souza
Centre for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom.
Sebastien
S
Ourselin
D000465:Algorithms; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D058958:Organs at Risk / Q000528:radiation effects; D011880:Radiotherapy Planning, Computer-Assisted; D050397:Radiotherapy, Intensity-Modulated / Q000009:adverse effects; D014057:Tomography, X-Ray Computed