title: Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model.
abstract: OBJECTIVE
  The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data.
  
  
  METHODS
  Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used to guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis.
  
  
  RESULTS
  The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the segmentation results significantly (p < 0.01) based on DSC (6.72%) and SD of contour-to-contour distances (0.08 cm) and decreased the 95% confidence intervals of the bladder volume differences. Moreover, expanding the shape model improved the segmentation results significantly (p < 0.01) based on DSC and SD of contour-to-contour distances.
  
  
  CONCLUSIONS
  This patient-specific shape model based automatic bladder segmentation method on CBCT is accurate and generic. Our segmentation method only needs two pretreatment imaging data sets as prior knowledge, is independent of patient gender and patient treatment position and has the possibility to manually adapt the segmentation locally.
authors.affiliation: Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
authors.forename: A J A J
authors.initials: AJ
authors.lastname: van de Schoot
authors.affiliation: Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
authors.forename: G
authors.initials: G
authors.lastname: Schooneveldt
authors.affiliation: Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
authors.forename: S
authors.initials: S
authors.lastname: Wognum
authors.affiliation: Department of Radiation Oncology, Daniel den Hoed Cancer Center, Erasmus Medical Center, Groene Hilledijk 301, 3075 EA Rotterdam, The Netherlands.
authors.forename: M S
authors.initials: MS
authors.lastname: Hoogeman
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Palo Alto, California 94305.
authors.forename: X
authors.initials: X
authors.lastname: Chai
authors.affiliation: Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
authors.forename: L J A
authors.initials: LJ
authors.lastname: Stalpers
authors.affiliation: Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
authors.forename: C R N
authors.initials: CR
authors.lastname: Rasch
authors.affiliation: Department of Radiation Oncology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
authors.forename: A
authors.initials: A
authors.lastname: Bel
mesh_terms: D054893:Cone-Beam Computed Tomography / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D015233:Models, Statistical; D056888:Patient Positioning; D010386:Pelvic Neoplasms / Q000532:radiotherapy*; D019047:Phantoms, Imaging; D016684:Prone Position; D061089:Radiotherapy, Image-Guided / Q000379:methods; D015203:Reproducibility of Results; D016683:Supine Position; D001743:Urinary Bladder / Q000000981:diagnostic imaging* / Q000528:radiation effects*