Abstract
BackgroundAccurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy. The accuracy of breast tissue segmentation is affected by the spatial distribution (or pattern) of fibroglandular tissue (FT). The goal of this study was to develop and evaluate texture features, determined from planning computed tomography (CT) data, to classify the spatial distribution of FT in the breast.MethodsPlanning CT data of 23 patients were evaluated in this study. Texture features were derived from the radial glandular fraction (RGF), which described the distribution of FT within three breast regions (posterior, middle, and anterior). Using visual assessment, experts grouped patients according to FT spatial distribution: sparse or non-sparse. Differences in the features between the two groups were investigated using the Wilcoxon rank test. Classification performance of the features was evaluated for a range of support vector machine (SVM) classifiers.ResultsExperts found eight patients and 15 patients had sparse and non-sparse spatial distribution of FT, respectively. A large proportion of features (>9 of 13) from the individual breast regions had significant differences (p <0.05) between the sparse and non-sparse group. The features from middle region had most significant differences and gave the highest classification accuracy for all the SVM kernels investigated. Overall, the features from middle breast region achieved highest accuracy (91 %) with the linear SVM kernel.ConclusionThis study found that features based on radial glandular fraction provide a means for discriminating between fibroglandular tissue distributions and could achieve a classification accuracy of 91 %.
Highlights
Accurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy
Radiotherapy is used to reduce the risk of local recurrence in early-stage breast cancer patients who have undergone breast-conserving surgery (BCS) [1]
Datasets were originally collected for a comparison of prone and supine positioning for breast radiotherapy [7, 8] which was approved by the Royal Marsden Committee for Clinical Research and the National
Summary
Accurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy. The accuracy of breast tissue segmentation is affected by the spatial distribution (or pattern) of fibroglandular tissue (FT). Partial Breast Irradiation (PBI) aims to irradiate only the volume of breast tissue surrounding the tumour bed (the region at higher risk of recurrence) rather than the whole breast to minimize. This could be addressed using adaptive radiotherapy, based on biomechanical modelling of breast tissue [3]. Biomechanical modelling requires accurate segmentation of breast tissue into its constituent components: fibroglandular tissue (FT) and adipose tissue [3, 4].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
