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Proceedings Paper

Automated segmentation of murine lung tumors in x-ray micro-CT images
Author(s): Joshua K. Y. Swee; Clare Sheridan; Elza de Bruin; Julian Downward; Francois Lassailly; Luis Pizarro
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Paper Abstract

Recent years have seen micro-CT emerge as a means of providing imaging analysis in pre-clinical study, with in-vivo micro-CT having been shown to be particularly applicable to the examination of murine lung tumors. Despite this, existing studies have involved substantial human intervention during the image analysis process, with the use of fully-automated aids found to be almost non-existent. We present a new approach to automate the segmentation of murine lung tumors designed specifically for in-vivo micro-CT-based pre-clinical lung cancer studies that addresses the specific requirements of such study, as well as the limitations human-centric segmentation approaches experience when applied to such micro-CT data. Our approach consists of three distinct stages, and begins by utilizing edge enhancing and vessel enhancing non-linear anisotropic diffusion filters to extract anatomy masks (lung/vessel structure) in a pre-processing stage. Initial candidate detection is then performed through ROI reduction utilizing obtained masks and a two-step automated segmentation approach that aims to extract all disconnected objects within the ROI, and consists of Otsu thresholding, mathematical morphology and marker-driven watershed. False positive reduction is finally performed on initial candidates through random-forest-driven classification using the shape, intensity, and spatial features of candidates. We provide validation of our approach using data from an associated lung cancer study, showing favorable results both in terms of detection (sensitivity=86%, specificity=89%) and structural recovery (Dice Similarity=0.88) when compared against manual specialist annotation.

Paper Details

Date Published: 18 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352L (18 March 2014); doi: 10.1117/12.2042443
Show Author Affiliations
Joshua K. Y. Swee, Imperial College London (United Kingdom)
Clare Sheridan, Cancer Research UK (United Kingdom)
Elza de Bruin, Cancer Research UK (United Kingdom)
Julian Downward, Cancer Research UK (United Kingdom)
Francois Lassailly, Cancer Research UK (United Kingdom)
Luis Pizarro, Univ. College London (United Kingdom)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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