Integrated Detection Network for Multiple Object Recognition

Recognizing multiple objects involves two interdependent tasks, object localization and classification. The goal of the object localization is to accurately find the object pose parameters relative to an established reference, such as the origin of the image coordinate system. The object classification assigns class labels to the objects according to the prespecified categories. Multiobject recognition has been previously solved by designing a set of individual single-object detectors or by training a combined multiobject detection and classification system. In the medical domain, these models can be further improved by relying on strong spatial prior information present in medical images of a human body. This chapter describes how the spatial prior can be used to recognize multiple anatomical structures, which results in the integrated detection network. The structures are recognized sequentially, one by one, using optimal order such that the later recognitions can benefit from constraints provided by previously recognized structures. The recognition relies on sequential estimation techniques, with the posterior distribution of the structure pose and label being approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The system is general and provides accurate recognitions of anatomical structures in 3D images of various modalities.