title: Semi-automatic segmentation of subcutaneous tumours from micro-computed tomography images.
abstract: This paper outlines the first attempt to segment the boundary of preclinical subcutaneous tumours, which are frequently used in cancer research, from micro-computed tomography (microCT) image data. MicroCT images provide low tissue contrast, and the tumour-to-muscle interface is hard to determine, however faint features exist which enable the boundary to be located. These are used as the basis of our semi-automatic segmentation algorithm. Local phase feature detection is used to highlight the faint boundary features, and a level set-based active contour is used to generate smooth contours that fit the sparse boundary features. The algorithm is validated against manually drawn contours and micro-positron emission tomography (microPET) images. When compared against manual expert segmentations, it was consistently able to segment at least 70% of the tumour region (n = 39) in both easy and difficult cases, and over a broad range of tumour volumes. When compared against tumour microPET data, it was able to capture over 80% of the functional microPET volume. Based on these results, we demonstrate the feasibility of subcutaneous tumour segmentation from microCT image data without the assistance of exogenous contrast agents. Our approach is a proof-of-concept that can be used as the foundation for further research, and to facilitate this, the code is open-source and available from www.setuvo.com.
authors.affiliation: Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
authors.forename: Rehan
authors.initials: R
authors.lastname: Ali
authors.forename: Cigdem
authors.initials: C
authors.lastname: Gunduz-Demir
authors.forename: Tünde
authors.initials: T
authors.lastname: Szilágyi
authors.forename: Ben
authors.initials: B
authors.lastname: Durkee
authors.forename: Edward E
authors.initials: EE
authors.lastname: Graves
mesh_terms: D000818:Animals; D001331:Automation; D045744:Cell Line, Tumor; D002471:Cell Transformation, Neoplastic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D051379:Mice; D064847:Multimodal Imaging; D018205:Neoplasms, Adipose Tissue / Q000000981:diagnostic imaging* / Q000473:pathology; D049268:Positron-Emission Tomography; D050151:Subcutaneous Fat / Q000000981:diagnostic imaging*; D055114:X-Ray Microtomography / Q000379:methods*