Effects of registration regularization and atlas sharpness on segmentation accuracy

Citation:

Yeo TBT, Sabuncu MR, Desikan R, Fischl B, Golland P. Effects of registration regularization and atlas sharpness on segmentation accuracy. Med Image Comput Comput Assist Interv. 2007;10 (Pt 1) :683-91.

Date Published:

2007

Abstract:

In this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of "sharpness" and the joint registration-segmentation of a new brain with these atlases. In non-rigid registration, the tradeoff between warp regularization and image fidelity is typically set empirically. In segmentation, this leads to a probabilistic atlas of arbitrary "sharpness": weak regularization results in well-aligned training images and a "sharp" atlas; strong regularization yields a "blurry" atlas. We study the effects of this tradeoff in the context of cortical surface parcellation by comparing three special cases of our framework, namely: progressive registration-segmentation of a new brain to increasingly "sharp" atlases with increasingly flexible warps; secondly, progressive registration to a single atlas with increasingly flexible warps; and thirdly, registration to a single atlas with fixed constrained warps. The optimal parcellation in all three cases corresponds to a unique balance of atlas "sharpness" and warp regularization that yield statistically significant improvements over the previously demonstrated parcellation results.
Last updated on 01/24/2017