Maysam Shahedi1, Martin Halicek2, Rongrong Guo1, Guoyi Zhang1, David M Schuster1, Baowei Fei1,2,3,4. 1. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30322, USA. 2. Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30332, USA. 3. Winship Cancer Institute of Emory University, Atlanta, GA, 30322, USA. 4. Department of Mathematics and Computer Science, Emory University, Atlanta, GA, 30322, USA.
Abstract
PURPOSE: Prostate segmentation in computed tomography (CT) images is useful for treatment planning and procedure guidance such as external beam radiotherapy and brachytherapy. However, because of the low, soft tissue contrast of CT images, manual segmentation of the prostate is a time-consuming task with high interobserver variation. In this study, we proposed a semiautomated, three-dimensional (3D) segmentation for prostate CT images using shape and texture analysis and we evaluated the method against manual reference segmentations. METHODS: The prostate gland usually has a globular shape with a smoothly curved surface, and its shape could be accurately modeled or reconstructed having a limited number of well-distributed surface points. In a training dataset, using the prostate gland centroid point as the origin of a coordination system, we defined an intersubject correspondence between the prostate surface points based on the spherical coordinates. We applied this correspondence to generate a point distribution model for prostate shape using principal component analysis and to study the local texture difference between prostate and nonprostate tissue close to the different prostate surface subregions. We used the learned shape and texture characteristics of the prostate in CT images and then combined them with user inputs to segment a new image. We trained our segmentation algorithm using 23 CT images and tested the algorithm on two sets of 10 nonbrachytherapy and 37 postlow dose rate brachytherapy CT images. We used a set of error metrics to evaluate the segmentation results using two experts' manual reference segmentations. RESULTS: For both nonbrachytherapy and post-brachytherapy image sets, the average measured Dice similarity coefficient (DSC) was 88% and the average mean absolute distance (MAD) was 1.9 mm. The average measured differences between the two experts on both datasets were 92% (DSC) and 1.1 mm (MAD). CONCLUSIONS: The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation.
PURPOSE: Prostate segmentation in computed tomography (CT) images is useful for treatment planning and procedure guidance such as external beam radiotherapy and brachytherapy. However, because of the low, soft tissue contrast of CT images, manual segmentation of the prostate is a time-consuming task with high interobserver variation. In this study, we proposed a semiautomated, three-dimensional (3D) segmentation for prostate CT images using shape and texture analysis and we evaluated the method against manual reference segmentations. METHODS: The prostate gland usually has a globular shape with a smoothly curved surface, and its shape could be accurately modeled or reconstructed having a limited number of well-distributed surface points. In a training dataset, using the prostate gland centroid point as the origin of a coordination system, we defined an intersubject correspondence between the prostate surface points based on the spherical coordinates. We applied this correspondence to generate a point distribution model for prostate shape using principal component analysis and to study the local texture difference between prostate and nonprostate tissue close to the different prostate surface subregions. We used the learned shape and texture characteristics of the prostate in CT images and then combined them with user inputs to segment a new image. We trained our segmentation algorithm using 23 CT images and tested the algorithm on two sets of 10 nonbrachytherapy and 37 postlow dose rate brachytherapy CT images. We used a set of error metrics to evaluate the segmentation results using two experts' manual reference segmentations. RESULTS: For both nonbrachytherapy and post-brachytherapy image sets, the average measured Dice similarity coefficient (DSC) was 88% and the average mean absolute distance (MAD) was 1.9 mm. The average measured differences between the two experts on both datasets were 92% (DSC) and 1.1 mm (MAD). CONCLUSIONS: The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation.
Authors: Maysam Shahedi; Derek W Cool; Cesare Romagnoli; Glenn S Bauman; Matthew Bastian-Jordan; Eli Gibson; George Rodrigues; Belal Ahmad; Michael Lock; Aaron Fenster; Aaron D Ward Journal: Med Phys Date: 2014-11 Impact factor: 4.071
Authors: Geert M Villeirs; Koen Van Vaerenbergh; Luc Vakaet; Samuel Bral; Filip Claus; Wilfried J De Neve; Koenraad L Verstraete; Gert O De Meerleer Journal: Strahlenther Onkol Date: 2005-07 Impact factor: 3.621
Authors: Lisa Jannicke Kjønigsen; Magnus Harneshaug; Ann-Monica Fløtten; Lena Korsmo Karterud; Kent Petterson; Grethe Skjolde; Heidi B Eggesbø; Harald Weedon-Fekjær; Hege Berg Henriksen; Peter M Lauritzen Journal: Eur Radiol Exp Date: 2019-10-30