Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate

Qi Song; Xiaodong Wu; Yunlong Liu; Mark Smith; John Buatti; Milan Sonka

doi:10.1007/978-3-642-04271-3_100

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Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate

Book chapter

Open access

Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate

Qi Song, Xiaodong Wu, Yunlong Liu, Mark Smith, John Buatti and Milan Sonka

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol.12(Pt 2), p.827

Lecture Notes in Computer Science, 5762, Springer

2009

DOI: 10.1007/978-3-642-04271-3_100

PMID: 20426188

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Published (Version of record) Open Access

Abstract

We present a novel method for globally optimal surface segmentation of multiple mutually interacting objects, incorporating both edge and shape knowledge in a 3-D graph-theoretic approach. Hard surface interacting constraints are enforced in the interacting regions, preserving the geometric relationship of those partially interacting surfaces. The soft smoothness a priori shape compliance is introduced into the energy functional to provide shape guidance. The globally optimal surfaces can be simultaneously achieved by solving a maximum flow problem based on an arc-weighted graph representation. Representing the segmentation problem in an arc-weighted graph, one can incorporate a wider spectrum of constraints into the formulation, thus increasing segmentation accuracy and robustness in volumetric image data. To the best of our knowledge, our method is the first attempt to introduce the arc-weighted graph representation into the graph-searching approach for simultaneous segmentation of multiple partially interacting objects, which admits a globally optimal solution in a low-order polynomial time. Our new approach was applied to the simultaneous surface detection of bladder and prostate. The result was quite encouraging in spite of the low saliency of the bladder and prostate in CT images.

Algorithms

Reproducibility of Results

Artificial Intelligence

Humans

Imaging, Three-Dimensional - methods

Radiographic Image Enhancement - methods

Male

Radiographic Image Interpretation, Computer-Assisted - methods

Prostate - diagnostic imaging

Sensitivity and Specificity

Urinary Bladder - diagnostic imaging

Prostatic Neoplasms - diagnostic imaging

Pattern Recognition, Automated - methods

Details

Title: Subtitle: Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate
Creators: Qi Song - Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA 52242, USA. qi-song@uiowa.edu
Xiaodong Wu
Yunlong Liu
Mark Smith
John Buatti
Milan Sonka
Resource Type: Book chapter
Publication Details: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol.12(Pt 2), p.827
Series: Lecture Notes in Computer Science; 5762
DOI: 10.1007/978-3-642-04271-3_100
PMID: 20426188
ISSN: 0302-9743
Publisher: Springer; Germany
Grant note: R01-EB004640 / NIBIB NIH HHS K25-CA123112 / NCI NIH HHS
Language: English
Date published: 2009
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Neurosurgery; Otolaryngology; Ophthalmology and Visual Sciences
Record Identifier: 9984040563402771

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