Automated segmentation and dose-volume analysis with DICOMautomaton

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

物理学;计算机科学

Clark, H.^1,2 ; Thomas, S.^3 ; Moiseenko, V.^2,4 ; Lee, R.^2 ; Gill, B.^2 ; Duzenli, C.^1,2 ; Wu, J.^2

University of British Columbia, Vancouver, BC, Canada^1

British Columbia Cancer Agency, Vancouver Centre, Vancouver, BC, Canada^2

British Columbia Cancer Agency, Fraser Valley Centre, Surrey, BC, Canada^3

University of California San Diego, San Diego, CA, United States^4

关键词: Automated segmentation;    Digital imaging and communication in medicines;    Dose-volume histograms;    Exploratory analysis;    Head-and-neck cancer;    Large amounts of data;    Segmentation techniques;    Varian medical systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012009/pdf
DOI  :  10.1088/1742-6596/489/1/012009

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Purpose: Exploration of historical data for regional organ dose sensitivity is limited by the effort needed to (sub-)segment large numbers of contours. A system has been developed which can rapidly perform autonomous contour sub-segmentation and generic dose-volume computations, substantially reducing the effort required for exploratory analyses. Methods: A contour-centric approach is taken which enables lossless, reversible segmentation and dramatically reduces computation time compared with voxel-centric approaches. Segmentation can be specified on a per-contour, per-organ, or per-patient basis, and can be performed along either an embedded plane or in terms of the contour's bounds (e.g., split organ into fractional-volume/dose pieces along any 3D unit vector). More complex segmentation techniques are available. Anonymized data from 60 head-and-neck cancer patients were used to compare dose-volume computations with Varian's EclipseTM(Varian Medical Systems, Inc.). Results: Mean doses and Dose-volume-histograms computed agree strongly with Varian's EclipseTM. Contours which have been segmented can be injected back into patient data permanently and in a Digital Imaging and Communication in Medicine (DICOM)-conforming manner. Lossless segmentation persists across such injection, and remains fully reversible. Conclusions: DICOMautomaton allows researchers to rapidly, accurately, and autonomously segment large amounts of data into intricate structures suitable for analyses of regional organ dose sensitivity.