Multi-Atlas Segmentation with Robust Label Transfer and Label Fusion

Abstract. Multi-atlas segmentation has been widely applied in medi-cal image analysis. This technique relies on image registration to transfer segmentation labels from pre-labeled atlases to a novel target image and applies label fusion to reduce errors produced by registration-based label transfer. To improve the performance of registration-based label transfer against registration errors, our first contribution is to propose a label transfer scheme that generates multiple warped versions of each atlas to one target image through registration paths obtained by composing inter-atlas registrations and atlas-target registrations. The problem of decreasing quality of warped atlases caused by accumulative errors in composing multiple registrations is properly addressed by an atlas selec-tion method that is guided by atlas segmentations. To improve the perfor-mance of label fusion against registration errors, our second contribution is to integrate the probabilistic correspondence model employed by the non-local mean approach with the joint label fusion technique, both of which have shown excellent performance for label fusion. Experiments on mitral-valve segmentation in 3D transesophageal echocardiography (TEE) show the effectiveness of the proposed techniques.