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12 March 2010 Model guided diffeomorphic demons for atlas based segmentation
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Abstract
Using an atlas, an image can be segmented by mapping its coordinate space to that of the atlas in an anatomically correct way. In order to find the correct mapping between the two different coordinate spaces e.g. diffeomorphic demons registration can be applied. The demons algorithm is a popular choice for deformable image registration and offers the possibility to perform computationally efficient non-rigid (diffeomorphic) registration. However, this registration method is prone to image artifacts and image noise. Therefore it has been the main objective of the presented work to combine the efficiency of diffeomorphic demons and the stability of statistical models. In the presented approach a statistical deformation model that describes "anatomically correct" displacements vector fields for a specific registration problem is used to guide the demons registration algorithm. By projecting the current displacement vector field, which is calculated during any iteration of the registration process, into the model space a regularized version of the vector field can be computed. Using this regularized vector field for the update of the deformation field in the subsequent iteration of the registration process the demons registration algorithm can be guided by the deformation model. The proposed method was evaluated on 21 CT datasets of the right hip. Measuring the average and maximum segmentation error for all 21 datasets and all 120 test configurations it could be demonstrated that the newly proposed algorithm leads to a reduction of the segmentation error of up to 13% compared to using the conventional diffeomorphic demons algorithm.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
K. D. Fritscher, B. Schuler, T. Roth, Ch. Kammerlander, M. Blauth, and R. Schubert "Model guided diffeomorphic demons for atlas based segmentation", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762306 (12 March 2010); https://doi.org/10.1117/12.844134
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