title: Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer.
abstract: For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.
authors.affiliation: Department of Radiation Oncology, Erasmus-MC Daniel den Hoed Cancer Center, Rotterdam, 3008 AE, The Netherlands. m.bondar@erasmusmc.nl
authors.forename: M Luiza
authors.initials: ML
authors.lastname: Bondar
authors.forename: Mischa
authors.initials: M
authors.lastname: Hoogeman
authors.forename: Wilco
authors.initials: W
authors.lastname: Schillemans
authors.forename: Ben
authors.initials: B
authors.lastname: Heijmen
mesh_terms: D001331:Automation; D002584:Cervix Uteri / Q000000981:diagnostic imaging* / Q000528:radiation effects; D005260:Female; D057918:Fiducial Markers; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards; D008954:Models, Biological; D009929:Organ Size; D011859:Radiography; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D013997:Time Factors; D001743:Urinary Bladder / Q000033:anatomy & histology; D002583:Uterine Cervical Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*