Semi-automatic segmentation of subcutaneous tumours from micro-computed tomography images.

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.

Curvatures with uncertainties derived in conformal space to characterize tendon microstructure.

There is a growing demand for non-invasive methods to diagnose tendon injuries and monitor the healing processes of their repair. To date there is limited knowledge on their structure and function and the interlink between these. One of the potential targets in this investigation is the extracellular matrix (ECM) that captures its structural changes. Recently we reported on the assessment tendon damage on a macroscopic level from high field MR signals. In this paper, we present a methodology that enables structural description on a microscopic level. We derived curvature values from the conformal monogenic signal, which however can become unreliable in the presence of noise. To account for this we use non parametric noise properties and a 1D feature based uncertainty measure in an iterative framework using Hidden Markov Measure Field (HMMF). The proposed method reveals that curvature values derived from normal tendon tissue microscopy images are higher and more homogenous than curvature values derived from the damaged tendon images.