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research.prior_knowledge [2012/12/10 22:36]
127.0.0.1 external edit
research.prior_knowledge [2019/08/07 16:01] (current)
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 (e.g., [Burschka 2005], [Mirota 2009], [Mirota 2011], [Uneri 2012]), video  (e.g., [Burschka 2005], [Mirota 2009], [Mirota 2011], [Uneri 2012]), video 
 has been fused with segmented volumetric models for skull base and head and neck surgery. has been fused with segmented volumetric models for skull base and head and neck surgery.
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 +======Anatomical Model Completion via Statistical Shape Models======
 +{{ :​research:​SSM:​icon_small.png?​nolink&​200}}
 +The current application of interest is Total Face Transplant Surgery. The primary intent of this project is to estimate missing anatomical structure using a statistical shape model (SSM). This may be applied in the presence of an incomplete medical image or when a patient has undergone severe trauma. With an estimate of the patient'​s true anatomy, we believe that a more complete and accurate surgical plan may be used. A known, and non-traumatized,​ portion of the transplant candidate'​s cranial structure is matched to a SSM of the entire cranial structure. The regions instantiated from the SSM corresponding to unknown, or traumatized,​ regions of the patient'​s anatomy are used to estimate the entire cranial structure of the patient. The major issue with the completion approach described in [Chintalapani 2010] is the presence of a non-smooth transition from the known region to the unknown region. We hoped that by modeling this estimation problem as a Multivariate Gaussian Regression problem would yield a smooth boundary, however this was not the case. We believe that asymmetries resulting from the pose of the patient'​s mandible are not accurately modeled by the primarily symmetrical “known” region of the neurocranium used as prior data. In order to avoid this problem, work is currently underway to create separate SSMs for the neurocranium and mandible.
 +[{{ :​intranet:​research:​SSM:​multivar_extrap_1.png?​400 |Figure 1: Extrapolation example with multivariate Gaussian regression; extrapolated area highlighted in green}}]
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 +Details are currently on the //​[[intranet:​research:​SSMModelCompletion|intranet page]]//.
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   * [Yao 2003a] J. Yao and R. H. Taylor, "​Non-Rigid Registration and Correspondence in Medical Image Analysis Using Multiple-Layer Flexible Mesh Template Matching",​ International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), vol. 17- 7, pp. 1145-1165, ​ 2003.   * [Yao 2003a] J. Yao and R. H. Taylor, "​Non-Rigid Registration and Correspondence in Medical Image Analysis Using Multiple-Layer Flexible Mesh Template Matching",​ International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), vol. 17- 7, pp. 1145-1165, ​ 2003.
   * [Yao 2003b] J. Yao and R. H. Taylor, "​Assessing Accuracy Factors in Deformable 2D/3D Medical Image Registration Using a Statistical Pelvis Model",​ in Ninth Int. Conference on Computer Vision, Nice, October 12-16, 2003.  pp. 1329-1334.  ​   * [Yao 2003b] J. Yao and R. H. Taylor, "​Assessing Accuracy Factors in Deformable 2D/3D Medical Image Registration Using a Statistical Pelvis Model",​ in Ninth Int. Conference on Computer Vision, Nice, October 12-16, 2003.  pp. 1329-1334.  ​
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research.prior_knowledge.txt · Last modified: 2019/08/07 16:01 (external edit)




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