Please use this identifier to cite or link to this item: https://doi.org/10.1109/JBHI.2013.2282183
Title: A spatiotemporal-based scheme for efficient registration-based segmentation of thoracic 4-D MRI
Authors: Yang, Y.
Van Reeth, E.
Poh, C.L.
Tan, C.H.
Tham, I.W.K. 
Keywords: Cancer
four-dimensional (4-D)
image registration
image segmentation
magnetic resonance imaging (MRI)
Issue Date: 2014
Citation: Yang, Y., Van Reeth, E., Poh, C.L., Tan, C.H., Tham, I.W.K. (2014). A spatiotemporal-based scheme for efficient registration-based segmentation of thoracic 4-D MRI. IEEE Journal of Biomedical and Health Informatics 18 (3) : 969-977. ScholarBank@NUS Repository. https://doi.org/10.1109/JBHI.2013.2282183
Abstract: Dynamic three-dimensional (3-D) (four-dimensional, 4-D) magnetic resonance (MR) imaging is gaining importance in the study of pulmonary motion for respiratory diseases and pulmonary tumor motion for radiotherapy. To perform quantitative analysis using 4-D MR images, segmentation of anatomical structures such as the lung and pulmonary tumor is required. Manual segmentation of entire thoracic 4-D MRI data that typically contains many 3-D volumes acquired over several breathing cycles is extremely tedious, time consuming, and suffers high user variability. This requires the development of new automated segmentation schemes for 4-D MRI data segmentation. Registration-based segmentation technique that uses automatic registration methods for segmentation has been shown to be an accurate method to segment structures for 4-D data series. However, directly applying registration-based segmentation to segment 4-D MRI series lacks efficiency. Here we propose an automated 4-D registration-based segmentation scheme that is based on spatiotemporal information for the segmentation of thoracic 4-D MR lung images. The proposed scheme saved up to 95% of computation amount while achieving comparable accurate segmentations compared to directly applying registration-based segmentation to 4-D dataset. The scheme facilitates rapid 3-D/4-D visualization of the lung and tumor motion and potentially the tracking of tumor during radiation delivery. © 2013 IEEE.
Source Title: IEEE Journal of Biomedical and Health Informatics
URI: http://scholarbank.nus.edu.sg/handle/10635/125350
ISSN: 21682194
DOI: 10.1109/JBHI.2013.2282183
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