Bootstrap a statistical brain atlas

Abstract

Registration of medical images enables quantitative study of anatomical differences between populations, as well as detection of abnormal variations indicative of pathologies. However inherent anatomical variabilities between individuals and possible pathologies make registration difficult. This paper presents a bootstrap strategy for characterizing non-pathological variations in human brain anatomy, as well its application to achieve accurate 3-D deformable registration. Inherent anatomical variations are initially extracted by deformably registering training data with an expert-segmented 3-D image, a digital brain atlas. Statistical properties of the density and geometric variations in brain anatomy are extracted and encoded into the atlas to build a statistical atlas. These statistics are then used as prior knowledge to guide the deformation process. A bootstrap loop is formed by registering the statistical atlas to larger training sets as more data becomes available, so as to ensure more robust knowledge extraction, and to achieve more precise registration. Compared to an algorithm with no knowledge guidance, registration using the statistical atlas reduces the overall error by 34%.