We introduce a new semantic features space able to encode spatial context via generalized geodesic distance transform. In this paper, we propose a new random forest-based segmentation framework for fetal 3D ultrasound volumes, able to efficiently integrate semantic and structural information in the classification process. 3D ultrasound has the potential to reduce the operator dependence. However, the accuracy of traditional 2D fetal biometrics is dependent on operator expertise and subjectivity in 2D plane finding and manual marking. Thanks to its non-invasive and non-ionizing properties, ultrasound allows quick, safe and detailed evaluation of the unborn baby, including the estimation of the gestational age, brain and cranium development. Ultrasound is the primary imaging method for prenatal screening and diagnosis of fetal anomalies.
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