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Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT

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Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT. / Mirzaei, Hamidresa; Tang, Lisa; Werner, Rene; Hamarneh, Ghassan.

Machine Learning in Medical Imaging. Hrsg. / Guorong Wu; Daoqiang Zhang; Dinggang Shen; Pingkun Yan; Kenji Suzuki; Fei Wang. Band 8184 1. Aufl. Springer, 2013. S. 179-186 (Lecture Notes in Computer Science).

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Harvard

Mirzaei, H, Tang, L, Werner, R & Hamarneh, G 2013, Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT. in G Wu, D Zhang, D Shen, P Yan, K Suzuki & F Wang (Hrsg.), Machine Learning in Medical Imaging. 1 Aufl., Bd. 8184, Lecture Notes in Computer Science, Springer, S. 179-186. https://doi.org/10.1007/978-3-319-02267-3_23

APA

Mirzaei, H., Tang, L., Werner, R., & Hamarneh, G. (2013). Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT. in G. Wu, D. Zhang, D. Shen, P. Yan, K. Suzuki, & F. Wang (Hrsg.), Machine Learning in Medical Imaging (1 Aufl., Band 8184, S. 179-186). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-02267-3_23

Vancouver

Mirzaei H, Tang L, Werner R, Hamarneh G. Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT. in Wu G, Zhang D, Shen D, Yan P, Suzuki K, Wang F, Hrsg., Machine Learning in Medical Imaging. 1 Aufl. Band 8184. Springer. 2013. S. 179-186. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-02267-3_23

Bibtex

@inbook{d9186a710ec64332870e91960ca73596,
title = "Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT",
abstract = "We propose an automatic lung tumor segmentation in dynamic CT images that incorporates the novel use of tumor tissue deformations. In contrast to elastography imaging techniques for measuring tumor tissue properties, which require mechanical compression and thereby interrupt normal breathing, we completely avoid the use of any external physical forces. Instead, we calculate the tissue deformations during normal respiration using deformable registration. We investigate machine learning methods in order to discover the spatio-temporal dynamics that would help distinguish tumor from normal tissue deformation patterns and integrate this information into the segmentation process. Our method adapts an ensemble of decision trees combined with a 3D graph-based optimization that takes into account spatio-temporal consistency. The experimental results on patients with large tumors achieved an average F-measure accuracy of 0.79.",
author = "Hamidresa Mirzaei and Lisa Tang and Rene Werner and Ghassan Hamarneh",
year = "2013",
doi = "10.1007/978-3-319-02267-3_23",
language = "English",
isbn = "978-3-319-02266-6",
volume = "8184",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "179--186",
editor = "Guorong Wu and Daoqiang Zhang and Dinggang Shen and Pingkun Yan and Kenji Suzuki and Fei Wang",
booktitle = "Machine Learning in Medical Imaging",
address = "Germany",
edition = "1",

}

RIS

TY - CHAP

T1 - Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT

AU - Mirzaei, Hamidresa

AU - Tang, Lisa

AU - Werner, Rene

AU - Hamarneh, Ghassan

PY - 2013

Y1 - 2013

N2 - We propose an automatic lung tumor segmentation in dynamic CT images that incorporates the novel use of tumor tissue deformations. In contrast to elastography imaging techniques for measuring tumor tissue properties, which require mechanical compression and thereby interrupt normal breathing, we completely avoid the use of any external physical forces. Instead, we calculate the tissue deformations during normal respiration using deformable registration. We investigate machine learning methods in order to discover the spatio-temporal dynamics that would help distinguish tumor from normal tissue deformation patterns and integrate this information into the segmentation process. Our method adapts an ensemble of decision trees combined with a 3D graph-based optimization that takes into account spatio-temporal consistency. The experimental results on patients with large tumors achieved an average F-measure accuracy of 0.79.

AB - We propose an automatic lung tumor segmentation in dynamic CT images that incorporates the novel use of tumor tissue deformations. In contrast to elastography imaging techniques for measuring tumor tissue properties, which require mechanical compression and thereby interrupt normal breathing, we completely avoid the use of any external physical forces. Instead, we calculate the tissue deformations during normal respiration using deformable registration. We investigate machine learning methods in order to discover the spatio-temporal dynamics that would help distinguish tumor from normal tissue deformation patterns and integrate this information into the segmentation process. Our method adapts an ensemble of decision trees combined with a 3D graph-based optimization that takes into account spatio-temporal consistency. The experimental results on patients with large tumors achieved an average F-measure accuracy of 0.79.

U2 - 10.1007/978-3-319-02267-3_23

DO - 10.1007/978-3-319-02267-3_23

M3 - SCORING: Contribution to collected editions/anthologies

SN - 978-3-319-02266-6

VL - 8184

T3 - Lecture Notes in Computer Science

SP - 179

EP - 186

BT - Machine Learning in Medical Imaging

A2 - Wu, Guorong

A2 - Zhang, Daoqiang

A2 - Shen, Dinggang

A2 - Yan, Pingkun

A2 - Suzuki, Kenji

A2 - Wang, Fei

PB - Springer

ER -