Regularised feature-based fuzzy connectedness segmentation of ultrasound images for fetal soft tissue quantification across gestation

Ultrasound (US) image segmentation can be a challenging task due to signal dropouts, missing boundaries, and presence of speckle. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes a novel US segmentation approach based on the fuzzy connectedness framework. The approach uses local phase and feature asymmetry to define the so-called affinity function, which drives the segmentation algorithm, incorporates a shape-based object completion step, and regularises the result by mean curvature flow. Identifying new image-based biomarkers of fetal nutrition across gestation is essential to characterise the well-being of a fetus at an early stage. Results are presented on US images of the fetal arm taken at multiple gestational ages, the adipose tissue being an indicator of the fetal nutritional state.