Investigation of the accuracy of breast tissue segmentation methods for the purpose of developing breast deformation models for use in adaptive radiotherapy

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Juneja, P.^1 ; Harris, E.J.^1 ; Evans, P.M.^2

Joint Department of Physics, Institute of Cancer Research, Sutton SM2 5PT, United Kingdom^1

CVSSP, University of Surrey, Guildford GU2 7XH, United Kingdom^2

关键词: Adaptive radiotherapy;    Fibroglandular tissue;    Fuzzy C means clustering;    K;    means clustering;    Realistic modelling;    Segmentation accuracy;    Segmentation methods;    Tissue distributions;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012030/pdf
DOI  :  10.1088/1742-6596/489/1/012030

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Realistic modelling of breast deformation requires the breast tissue to be segmented into fibroglandular and fatty tissue and assigned suitable material properties. There are a number of breast tissue segmentation methods proposed and used in the literature. The purpose of this study was to validate and compare the accuracy of various segmentation methods and to investigate the effect of the tissue distribution on the segmentation accuracy. Computed tomography (CT) data for 24 patients, both in supine and prone positions were segmented into fibroglandular and fatty tissue. The segmentation methods explored were: physical density thresholding; interactive thresholding; fuzzy c-means clustering (FCM) with three classes (FCM3) and four classes (FCM4); and k-means clustering. Validation was done in two-stages: firstly, a new approach, supine-prone validation based on the assumption that the breast composition should appear the same in the supine and prone scans was used. Secondly, outlines from three experts were used for validation. This study found that FCM3 gave the most accurate segmentation of breast tissue from CT data and that the segmentation accuracy is adversely affected by the sparseness of the fibroglandular tissue distribution.