9 March 2011 Lung tumours segmentation on CT using sparse field active model
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Abstract
Three-dimensional (3D) manual segmentation of lung tumours is observer-dependent and time consuming, which are major limitations for use in clinical trials. In this paper we present a semi-automated 3D segmentation method, which is more time-efficient and less operator dependent than manual segmentation. We developed a semi-automated algorithm to segment lung tumours on chest computed tomography (CT) images using shape constrained multi-thresholding (SCMT) and sparse field active model (SFAM) techniques. For each 2D slice of CT tumour image, an initial contour was generated using SCMT. This initial contour was then deformed using SFAM. Seven energies were utilized in the SFAM technique to control the deformation namely: global region, local region, curvature, edge information, smoothness, anchor, and partial volume. The proposed algorithm was tested with 70 CT tumour slices (19 well-defined tumours (WD) located centrally in the lung parenchyma without significant vasculature and 51 vascularized or juxtapleural tumours (VJ)). Our results showed that the initial contour generated by the SCMT technique was sufficient to segment the well-defined (WD) tumours without any deformation. However, the deformation using SFAM was required to segment vascularized or juxtapleural (VJ) tumours. The results of the proposed segmentation algorithm were evaluated by comparing them to manual segmentation using the dice coefficient (DC). The average DC was 96.3±1.1% and 95.2±1.6% for WD and VJ tumour images respectively. The average DC obtained for the entire data set was 95.5±1.6%, which shows that the proposed algorithm can accurately segment lung tumours and can be utilized for monitoring tumours response to treatment.
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Joseph Awad, Joseph Awad, Laura Wilson, Laura Wilson, Grace Parraga, Grace Parraga, Aaron Fenster, Aaron Fenster, } "Lung tumours segmentation on CT using sparse field active model", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79632Y (9 March 2011); doi: 10.1117/12.877566; https://doi.org/10.1117/12.877566
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