Part-Based Object Detection and Segmentation

Abstract

The computational framework of Marginal Space Learning (MSL) formulates the detection of an anatomical structure as a whole object. Nevertheless, an object might exhibit different degrees of variations due to many factors, e.g., nonrigid deformation, anatomical variations, or diversity in scanning protocols. When variations are too large for the object to maintain a globally consistent shape or appearance, the MSL cannot be applied directly or the detection robustness may be degraded. In such scenarios the use of part based models is recommended. Different applications may demand different methods to split the object into parts, enforce constraint during part detection, and aggregate the results of part detectors. In this chapter, we demonstrate the robustness and accuracy of part-based object detection and segmentation on three applications, namely, left atrium segmentation in 3D C-arm CT, left ventricle detection in 2D MRI, and aorta segmentation in 3D C-arm CT.