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Head and neck multi‐organ auto‐segmentation on CT images aided by synthetic MRI
Author(s) -
Liu Yingzi,
Lei Yang,
Fu Yabo,
Wang Tonghe,
Zhou Jun,
Jiang Xiaojun,
McDonald Mark,
Beitler Jonathan J.,
Curran Walter J.,
Liu Tian,
Yang Xiaofeng
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14378
Subject(s) - contouring , segmentation , head and neck , hausdorff distance , nuclear medicine , medicine , optic chiasm , artificial intelligence , computer science , anatomy , optic nerve , surgery , computer graphics (images)
Purpose Because the manual contouring process is labor‐intensive and time‐consuming, segmentation of organs‐at‐risk (OARs) is a weak link in radiotherapy treatment planning process. Our goal was to develop a synthetic MR (sMR)‐aided dual pyramid network (DPN) for rapid and accurate head and neck multi‐organ segmentation in order to expedite the treatment planning process. Methods Forty‐five patients’ CT, MR, and manual contours pairs were included as our training dataset. Nineteen OARs were target organs to be segmented. The proposed sMR‐aided DPN method featured a deep attention strategy to effectively segment multiple organs. The performance of sMR‐aided DPN method was evaluated using five metrics, including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and volume difference. Our method was further validated using the 2015 head and neck challenge data. Results The contours generated by the proposed method closely resemble the ground truth manual contours, as evidenced by encouraging quantitative results in terms of DSC using the 2015 head and neck challenge data. Mean DSC values of 0.91 ± 0.02, 0.73 ± 0.11, 0.96 ± 0.01, 0.78 ± 0.09/0.78 ± 0.11, 0.88 ± 0.04/0.88 ± 0.06 and 0.86 ± 0.08/0.85 ± 0.1 were achieved for brain stem, chiasm, mandible, left/right optic nerve, left/right parotid, and left/right submandibular, respectively. Conclusions We demonstrated the feasibility of sMR‐aided DPN for head and neck multi‐organ delineation on CT images. Our method has shown superiority over the other methods on the 2015 head and neck challenge data results. The proposed method could significantly expedite the treatment planning process by rapidly segmenting multiple OARs.