Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images
- 1 January 2008
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC in Lecture Notes in Computer Science
- Vol. 11, 502-509
- https://doi.org/10.1007/978-3-540-85988-8_60
Abstract
Hierarchical multi-organ statistical atlases are constructed with the aim of achieving fully automated segmentation of the liver and related organs from computed tomography images. Constraints on inter-relations among organs are embedded in hierarchical organization of probabilistic atlases (PAs) and statistical shape models (SSMs). Hierarchical PAs are constructed based on the hierarchical nature of inter-organ relationships. Multi-organ SSMs (MO-SSMs) are combined with previously proposed single-organ multi-level SSMs (ML-SSMs). A hierarchical segmentation procedure is then formulated using the constructed hierarchical atlases. The basic approach consists of hierarchical recursive processes of initial region extraction using PAs and subsequent refinement using ML/MO-SSMs. The experimental results show that segmentation accuracy of the liver was improved by incorporating constraints on inter-organ relationships.This publication has 6 references indexed in Scilit:
- Automated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multi-level Statistical Shape ModelLecture Notes in Computer Science, 2007
- Constructing a Probabilistic Model for Automated Liver Region Segmentation Using Non-contrast X-Ray Torso CT imagesLecture Notes in Computer Science, 2006
- Liver Segmentation in Living Liver Transplant Donors: Comparison of Semiautomatic and Manual MethodsRadiology, 2005
- Neighbor-Constrained Segmentation With Level Set Based 3-D Deformable ModelsIEEE Transactions on Medical Imaging, 2004
- Construction of an abdominal probabilistic atlas and its application in segmentationIEEE Transactions on Medical Imaging, 2003
- A new point matching algorithm for non-rigid registrationComputer Vision and Image Understanding, 2003