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Neurosonography is the most widely used imaging technique for assessing neuro-development of the growing fetus in clinical practice. 3D neurosonography has an advantage of quick acquisition but is yet to demonstrate improvements in clinical workflow. In this paper we propose an automatic technique to segment four important fetal brain structures in 3D ultrasound. The technique is built within a Random Decision Forests framework. Our solution includes novel pre-processing and new features. The pre-processing step makes sure that all volumes are in the same coordinate. The new features constrain the appearance framework by adding a novel distance feature. Validation on 51 3D fetal neurosonography images shows that the proposed technique is capable of segmenting fetal brain structures and providing promising qualitative and quantitative results. © 2013 Springer International Publishing.

Original publication




Conference paper

Publication Date



8184 LNCS


25 - 32