Effects of registration regularization and atlas sharpness on segmentation accuracy

Authors:
B. T. Thomas Yeo;Mert R. Sabuncu;Rahul Desikan;Bruce Fischl;Polina Golland
Affiliations:
Computer Science and Artificial Intelligence Lab, MIT;Computer Science and Artificial Intelligence Lab, MIT;Boston University School of Medicine;Athinoula A. Martinos Center for Biomedical Imaging, MGH, MIT, HMS;Computer Science and Artificial Intelligence Lab, MIT
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

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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.