Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy

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 warp...

• Type: Conference
• Year: 2007
• Where: MICCAI
• Authors: B. T. Thomas Yeo, Mert R. Sabuncu, Rahul Desikan, Bruce Fischl, Polina Golland
• Keywords: Fixed Constrained Warps, Medical Imaging, MICCAI 2007, Strong Regularization, Warp Regularization