Structural information and (hyper)graph matching for MRI piglet brain extraction

Abstract

In the context of the study of the maturation process of the infant brain, this paper focuses on postnatal piglet brain, whose structure is similar to the one of an infant. Due to the small size of the piglet brain and the abundance of surrounding fat and muscles, the automatic brain extraction using correctely initialized deformable models is tedious, and the standard approach used for human brain does not apply. The paper proposes an original brain extraction method based on a deformable model, whose initialization is guided by a priori known relationships between some anatomical structures of the head. This concerns a structural model related to a priori known inclusion and photometric relationships between eyes, nose and other internal head entities (fat and muscles). This a priori structural information also involves the relative position of both eyes and nose, assumed to be an anatomical invariant similar to a triangle. Using this structural model, our proposal detects both eyes and nose, from which one deduces the brain center, for finally initializing deformable models. Anatomical structures are retrieved by matching observed relationships with those embedded in the a priori structural model. This involves graph and hypergraph matching, where hypergraph matching concerns relative position of eyes and nose (ternary constraint related to these 3 entities). The method has been implemented and preliminary experiments have been performed on a set of 6 piglets, to evaluate the accuracy of the brain center localization, the one of the final brain extraction using deformable models. The brain center is correctly localized with a mean error of 1.7 mm, underlying the relevance of the approach. The mean similarity index has been measured to be of 0.85 (with a standard deviation of 0.04). More generally, this work illustrates the potential of considering high level a priori known relationships, related to anatomical invariants, managed using graph and hypergraph matching.