A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Segmentation of Clinical Structures from Images of the Human Pelvic Area
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Model-Based Multi-Object Segmentation via Distribution Matching
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Coupled PDEs for Non-Rigid Registration and Segmentation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Coupled Parametric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of medical images using a genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fast elastic registration for adaptive radiotherapy
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Prostate segmentation using pixel classification and genetic algorithms
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Histogram statistics of local model-relative image regions
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
Constrained surface evolutions for prostate and bladder segmentation in CT images
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Coupling Deformable Models for Multi-object Segmentation
ISBMS '08 Proceedings of the 4th international symposium on Biomedical Simulation
MRI Bone Segmentation Using Deformable Models and Shape Priors
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Active Volume Models with Probabilistic Object Boundary Prediction Module
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
3D Meshless Prostate Segmentation and Registration in Image Guided Radiotherapy
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: 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
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Interactive segmentation of volumetric medical images for collaborative telemedicine
3DPH'09 Proceedings of the 2009 international conference on Modelling the Physiological Human
Computer Methods and Programs in Biomedicine
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In this paper, we propose a fully automatic method for the coupled 3D localization and segmentation of lower abdomen structures. We apply it to the joint segmentation of the prostate and bladder in a database of CT scans of the lower abdomen of male patients. A flexible approach on the bladder allows the process to easily adapt to high shape variation and to intensity inhomogeneities that would be hard to characterize (due, for example, to the level of contrast agent that is present). On the other hand, a statistical shape prior is enforced on the prostate. We also propose an adaptive non-overlapping constraint that arbitrates the evolution of both structures based on the availability of strong image data at their common boundary. The method has been tested on a database of 16 volumetric images, and the validation process includes an assessment of inter-expert variability in prostate delineation, with promising results.