Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Active shape models—their training and application
Computer Vision and Image Understanding
Efficient kernel density estimation of shape and intensity priors for level set segmentation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
IEEE Transactions on Image Processing
Spine Segmentation Using Articulated Shape Models
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Automatic segmentation of bladder and prostate using coupled 3D deformable models
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Graph search with appearance and shape information for 3-D prostate and bladder segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
MICCAI'11 Proceedings of the 2011 international conference on Prostate cancer imaging: image analysis and image-guided interventions
Computer Methods and Programs in Biomedicine
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Computers in Biology and Medicine
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We propose a Bayesian formulation for coupled surface evolutions and apply it to the segmentation of the prostate and the bladder in CT images. This is of great interest to the radiotherapy treatment process, where an accurate contouring of the prostate and its neighboring organs is needed. A purely data based approach fails, because the prostate boundary is only partially visible. To resolve this issue, we define a Bayesian framework to impose a shape constraint on the prostate, while coupling its extraction with that of the bladder. Constraining the segmentation process makes the extraction of both organs’ shapes more stable and more accurate. We present some qualitative and quantitative results on a few data sets, validating the performance of the approach.