MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Statistical Location Model for Abdominal Organ Localization
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Liver Segmentation Using Automatically Defined Patient Specific B-Spline Surface Models
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
Computation and evaluation of medial surfaces for shape representation of abdominal organs
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
Analyses of missing organs in abdominal multi-organ segmentation
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
A validation benchmark for assessment of medial surface quality for medical applications
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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Abdominal organ localization is required as an initialization step for most automated abdominal organ analysis tasks, i.e. segmentation, registration, and computer aided-diagnosis. Automated abdominal organ localization is difficult because of the large variability of organ shapes, similar appearances of different organs in images, and organs in close proximity to each other. Previous methods predicted only the organ locations, but not the full organ poses including additionally sizes and orientations. Thus they were often not accurate enough to initialize other image analysis tasks. In this work we proposed a maximum a posteriori (MAP) framework to estimate the poses of multiple abdominal organs from non-contrast CT images. A novel organ pose distribution model is proposed to model the organ poses and limit the search space. Additionally the method uses probabilistic atlases for organ shapes, and Gaussian mixture models for organ intensity profile. An MAP problem is then formulated and solved for organ poses. The method was applied for the localization of liver, left and right kidneys, spleen, and pancreas, and showed promising results, especially on liver and spleen (with mean location and orientation errors under 5.3 mm and 7 degrees respectively).