Book chapter
Surface–Region Context in Optimal Multi-object Graph-Based Segmentation: Robust Delineation of Pulmonary Tumors
Information Processing in Medical Imaging, pp.61-72
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2011
DOI: 10.1007/978-3-642-22092-0_6
PMCID: PMC3158678
PMID: 21761646
Abstract
Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object–surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76±0.10) was improved to 0.84±0.05 when employing our new method for pulmonary tumor segmentation.
Details
- Title: Subtitle
- Surface–Region Context in Optimal Multi-object Graph-Based Segmentation: Robust Delineation of Pulmonary Tumors
- Creators
- Qi Song - Department of Electrical & Computer Engineering, University of Iowa, Iowa City, USAMingqing Chen - Department of Electrical & Computer Engineering, University of Iowa, Iowa City, USAJunjie Bai - Department of Electrical & Computer Engineering, University of Iowa, Iowa City, USAMilan Sonka - Department of Ophthalmology & Visual Sciences, University of Iowa, Iowa City, USAXiaodong Wu - Department of Radiation Oncology, University of Iowa, Iowa City, USA
- Resource Type
- Book chapter
- Publication Details
- Information Processing in Medical Imaging, pp.61-72
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-642-22092-0_6
- PMID
- 21761646
- PMCID
- PMC3158678
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Language
- English
- Date published
- 2011
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984046810802771
Metrics
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