Active shape models—their training and application
Computer Vision and Image Understanding
Shape Constrained Deformable Models for 3D Medical Image Segmentation
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Volumetric medical images segmentation using shape constrained deformable models
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
3D active shape models using gradient descent optimization of description length
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation
Computers in Biology and Medicine
A Bayesian Approach for Liver Analysis: Algorithm and Validation Study
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Model-Based Segmentation Using Graph Representations
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Sample Sufficiency and Number of Modes to Retain in Statistical Shape Modelling
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
An improved level set for liver segmentation and perfusion analysis in MRIs
IEEE Transactions on Information Technology in Biomedicine
Liver segmentation using sparse 3D prior models with optimal data support
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Organ pose distribution model and an MAP framework for automated abdominal multi-organ localization
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Automated segmentation of 3D CT images based on statistical atlas and graph cuts
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Automated PET-guided liver segmentation from low-contrast CT volumes using probabilistic atlas
Computer Methods and Programs in Biomedicine
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This paper presents an evaluation of the performance of a three-dimensional Active Shape Model (ASM) to segment the liver in 48 clinical CT scans. The employed shape model is built from 32 samples using an optimization approach based on the minimum description length (MDL). Three different gray-value appearance models (plain intensity, gradient and normalized gradient profiles) are created to guide the search. The employed segmentation techniques are ASM search with 10 and 30 modes of variation and a deformable model coupled to a shape model with 10 modes of variation. To assess the segmentation performance, the obtained results are compared to manual segmentations with four different measures (overlap, average distance, RMS distance and ratio of deviations larger 5mm). The only appearance model delivering usable results is the normalized gradient profile. The deformable model search achieves the best results, followed by the ASM search with 30 modes. Overall, statistical shape modeling delivers very promising results for a fully automated segmentation of the liver.