1. Semi-automatic segmentation of subcutaneous tumours from micro-computed tomography images
- Author
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Cigdem Gunduz-Demir, Edward E. Graves, Rehan Ali, Ben Y. Durkee, and Tunde Szilagyi
- Subjects
Male ,Open-source ,X-ray microtomography ,subcutaneous fat ,Computer science ,cell transformation ,multimodal imaging ,Multimodal Imaging ,Automation ,Mice ,Image Processing, Computer-Assisted ,Segmentation ,Computer vision ,animal ,micro-computed tomography ,Active contour model ,Image segmentation ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,Active contours ,article ,methodology ,Contrast agent ,Cell Transformation, Neoplastic ,Positron emission tomography ,scintiscanning ,Tomography ,radiography ,Emission tomography ,Subcutaneous Fat ,lipoma ,Cancer research ,Article ,Proof of concept ,Microcomputed tomography ,Open - source ,Level set ,Tumors active contours ,Cell Line, Tumor ,medicine ,Animals ,Humans ,Radiology, Nuclear Medicine and imaging ,human ,mouse ,Neoplasms, Adipose Tissue ,Feature detection (computer vision) ,Tumors ,Semi-automatic segmentation ,business.industry ,Semi - automatic Segmentation ,tumor cell line ,X-Ray Microtomography ,Computerized tomography ,image processing ,Positron-Emission Tomography ,pathology ,Artificial intelligence ,business ,Emission computed tomography - 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. © 2013 Institute of Physics and Engineering in Medicine.
- Published
- 2013