A Gaussian mixture + demons deformable registration method for cone-beam CT-guided robotic transoral base-of-tongue surgery

S. Reaungamornrat, W. P. Liu, S. Schafer, Y. Otake, S. Nithiananthan, A. Uneri, Jeremy D Richmon, J. Sorger, J. H. Siewerdsen, R. H. Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Purpose: An increasingly popular minimally invasive approach to resection of oropharyngeal / base-of-tongue cancer is made possible by a transoral technique conducted with the assistance of a surgical robot. However, the highly deformed surgical setup (neck flexed, mouth open, and tongue retracted) compared to the typical patient orientation in preoperative images poses a challenge to guidance and localization of the tumor target and adjacent critical anatomy. Intraoperative cone-beam CT (CBCT) can account for such deformation, but due to the low contrast of soft-tissue in CBCT images, direct localization of the target and critical tissues in CBCT images can be difficult. Such structures may be more readily delineated in preoperative CT or MR images, so a method to deformably register such information to intraoperative CBCT could offer significant value. This paper details the initial implementation of a deformable registration framework to align preoperative images with the deformed intraoperative scene and gives preliminary evaluation of the geometric accuracy of registration in CBCT-guided TORS. Method: The deformable registration aligns preoperative CT or MR to intraoperative CBCT by integrating two established approaches. The volume of interest is first segmented (specifically, the region of the tongue from the tip to the hyoid), and a Gaussian mixture (GM) mode1 of surface point clouds is used for rigid initialization (GMRigid) as well as an initial deformation (GMNonRigid). Next, refinement of the registration is performed using the Demons algorithm applied to distance transformations of the GM-registered and CBCT volumes. The registration accuracy of the framework was quantified in preliminary studies using a cadaver emulating preoperative and intraoperative setups. Geometric accuracy of registration was quantified in terms of target registration error (TRE) and surface distance error. Result: With each step of the registration process, the framework demonstrated improved registration, achieving mean TRE of 3.0 mm following the GM rigid, 1.9 mm following GM nonrigid, and 1.5 mm at the output of the registration process. Analysis of surface distance demonstrated a corresponding improvement of 2.2, 0.4, and 0.3 mm, respectively. The evaluation of registration error revealed the accurate alignment in the region of interest for base-of-tongue robotic surgery owing to point-set selection in the GM steps and refinement in the deep aspect of the tongue in the Demons step. Conclusions: A promising framework has been developed for CBCT-guided TORS in which intraoperative CBCT provides a basis for registration of preoperative images to the highly deformed intraoperative setup. The registration framework is invariant to imaging modality (accommodating preoperative CT or MR) and is robust against CBCT intensity variations and artifact, provided corresponding segmentation of the volume of interest. The approach could facilitate overlay of preoperative planning data directly in stereo-endoscopic video in support of CBCT-guided TORS.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2013
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
StatePublished - Jun 10 2013
EventMedical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling - Lake Buena Vista, FL, United States
Duration: Feb 12 2013Feb 14 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherMedical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
Country/TerritoryUnited States
CityLake Buena Vista, FL


  • Cone-beam CT
  • Deformable registration
  • Demons algorithm
  • Gaussian mixture model
  • Image-guidance
  • Robotic surgery
  • Video augmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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