Reliability-based robust multi-atlas label fusion for brain MRI segmentation.

Label fusion is one of the key steps in multi-atlas based segmentation of structural magnetic resonance (MR) images. Although a number of label fusion methods have been developed in literature, most of those existing methods fail to address two important problems, i.e., (1) compared with boundary voxels, inner voxels usually have higher probability (or reliability) to be correctly segmented, and (2) voxels with high segmentation reliability (after initial segmentation) can help refine the segmentation of voxels with low segmentation reliability in the target image. To this end, we propose a general reliability-based robust label fusion framework for multi-atlas based MR image segmentation. Specifically, in the first step, we perform initial segmentation for MR images using a conventional multi-atlas label fusion method. In the second step, for each voxel in the target image, we define two kinds of reliability, including the label reliability and spatial reliability that are estimated based on the soft label and spatial information from the initial segmentation, respectively. Finally, we employ voxels with high label-spatial reliability to help refine the label fusion process of those with low reliability in the target image. We incorporate our proposed framework into four well-known label fusion methods, including locally-weighted voting (LWV), non-local mean patch-based method (PBM), joint label fusion (JLF) and sparse patch-based method (SPBM), and obtain four novel label-spatial reliability-based label fusion approaches (called ls-LWV, ls-PBM, ls-JLF, and ls-SPBM). We validate the proposed methods in segmenting ROIs of brain MR images from the NIREP, LONI-LPBA40 and ADNI datasets. The experimental results demonstrate that our label-spatial reliability-based label fusion methods outperform the state-of-the-art methods in multi-atlas image segmentation.