Abstract
Segmentation of prostate Cone Beam CT (CBCT) images is an essential step towards real-time adaptive radiotherapy (ART). It is challenging for Calypso patients, as more artifacts generated by the beacon transponders are present on the images. We herein propose a novel wavelet-based segmentation algorithm for rectum, bladder, and prostate of CBCT images with implanted Calypso transponders. For a given CBCT, a Moving Window-Based Double Haar (MWDH) transformation is applied first to obtain the wavelet coefficients. Based on a user defined point in the object of interest, a cluster algorithm based adaptive thresholding is applied to the low frequency components of the wavelet coefficients, and a Lee filter theory based adaptive thresholding is applied on the high frequency components. For the next step, the wavelet reconstruction is applied to the thresholded wavelet coefficients. A binary (segmented) image of the object of interest is therefore obtained. 5 hypofractionated Calypso prostate patients with daily CBCT were studied. DICE, Sensitivity, Inclusiveness and ΔV were used to evaluate the segmentation result.
Highlights
One important step of the Adaptive Radiation Therapy (ART) is the segmentation of the Cone Beam CT (CBCT) images—a step required for the adaptive planning
Segmentation of prostate Cone Beam CT (CBCT) images is an essential step towards real-time adaptive radiotherapy (ART)
Based on a user defined point in the object of interest, a cluster algorithm based adaptive thresholding is applied to the low frequency components of the wavelet coefficients, and a Lee filter theory based adaptive thresholding is applied on the high frequency components
Summary
One important step of the Adaptive Radiation Therapy (ART) is the segmentation of the CBCT images—a step required for the adaptive planning. This is specially important for hypofractionated treatments. Many studies have been published on automatic segmentation of prostate CBCT [1]-[9]. No study was published yet on the segmentation of prostate CBCT with implanted Calypso transponders. We use a combination of these two—the Moving window-based Double Haar (MWDH) transformation for our prostate segmentation. The segmented result is obtained by wavelet reconstruction of the thresholded components. The rest of this paper is organized as follows: in section 2, we will present the wavelet based segmentation algorithm in more details.
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