Statistical Location Model for Abdominal Organ Localization

Authors:
Jianhua Yao;Ronald M. Summers
Affiliations:
The National Institutes of Health, Imaging Biomarkers and Computer Aided Diagnosis Lab, Clinical Center, Bethesda, USA 20892;The National Institutes of Health, Imaging Biomarkers and Computer Aided Diagnosis Lab, Clinical Center, Bethesda, USA 20892
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

Citing 3
Cited 4

Active shape models—their training and application

Computer Vision and Image Understanding
Geodesic active contours

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - 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
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

Quantified Score

Hi-index	0.00

Visualization

Abstract

Initial placement of the models is an essential pre-processing step for model-based organ segmentation. Based on the observation that organs move along with the spine and their relative locations remain relatively stable, we built a statistical location model (SLM) and applied it to abdominal organ localization. The model is a point distribution model which learns the pattern of variability of organ locations relative to the spinal column from a training set of normal individuals. The localization is achieved in three stages: spine alignment, model optimization and location refinement. The SLM is optimized through maximum a posteriori estimation of a probabilistic density model constructed for each organ. Our model includes five organs: liver, left kidney, right kidney, spleen and pancreas. We validated our method on 12 abdominal CTs using leave-one-out experiments. The SLM enabled reduction in the overall localization error from 62.0±28.5 mm to 5.8±1.5 mm. Experiments showed that the SLM was robust to the reference model selection.