Two-stage fusion set selection in multi-atlas-based image segmentation

Conventional multi-atlas-based segmentation demands pairwise full-fledged registration between each atlas image and the target image, which leads to high computational cost and poses great challenge in the new era of big data. On the other hand, only the most relevant atlases should contribute to final label fusion. In this work, we introduce a two-stage fusion set selection method by first trimming the atlas collection into an augmented subset based on a low-cost registration and the preliminary relevance metric, followed by a further refinement based on a full-fledged registration and the corresponding relevance metric. A statistical inference model is established to relate the preliminary and the refined relevance metrics, and a proper augmented subset size is derived based on it. Empirical evidence supported the inference model, and end-to-end performance assessment demonstrated the proposed scheme to be computationally efficient without compromising segmentation accuracy.