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Radiology Research and Practice
Volume 2014 (2014), Article ID 547075, 9 pages
Research Article

Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours

1Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
2Department of Human Oncology, University of Wisconsin-Madison, Madison, WI 53792, USA
3Wisconsin Institute of Medical Research, 1111 Highland Avenue, Madison, WI 53705, USA
4Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53792, USA
5Department of Electrical & Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
6Department of Radiation Oncology, Intermountain Healthcare, Salt Lake City, UT 84107, USA

Received 18 March 2014; Revised 2 July 2014; Accepted 4 July 2014; Published 3 August 2014

Academic Editor: Sotirios Bisdas

Copyright © 2014 Venkata V. Chebrolu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (using and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods. Results. MPSL algorithm segmented the GTV in 4DCT images in seconds per phase ( resolution) as compared to seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were . In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051. Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.