This paper outlines the first attempt to segment the boundary of preclinical subcutaneous tumors, which are frequently used in cancer research, from micro-Computed Tomography (microCT) image data. MicroCT images provide low tissue contrast, and the tumor-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 microPET images. When compared against manual expert segmentations, it was consistently able to segment at least 70% of the tumor region (n=39) in both easy and difficult cases, and over a broad range of tumor volumes. When compared against tumor microPET data, it was able to capture over 80% of the functional microPET volume. Based on these results, we demonstrate the feasibility of subcutaneous tumor 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 .
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