Oriented active shape models for 3D segmentation in medical images

Active Shape Models (ASM) have been applied to various segmentation tasks in medical imaging, most successfully in 2D segmentation of objects that have a fairly consistent shape. However, several difficulties arise when extending 2D ASM to 3D: (1) difficulty in 3D labeling, (2) the requirement of a large number of training samples, (3) the challenging problem of landmark correspondence in 3D, (4) inefficient initialization and optimization in 3D. This paper addresses the 3D segmentation problem by using a small number of effective 2D statistical models called oriented ASM (OASM). We demonstrate that a small number of 2D OASM models, which are derived from a chunk of a contiguous set of slices, are sufficient to capture the shape variation between slices and individual objects. Each model can be matched rapidly to a new slice by using the OASM algorithm 1 . Our experiments in segmenting breast and bone of the foot in MR images indicate the following: (1) The accuracy of segmentation via our method is much better than that of 2DASM-based segmentation methods. 2 (2) Far fewer landmarks are required compared with thousands of landmarks needed in true 3D ASM. Therefore, far fewer training samples are required to capture details. (3) Our method is computationally slightly more expensive than the 2D method2 owing to its 2 level dynamic programming (2LDP) algorithm.