Joshi Anand A, Shattuck David W, Leahy Richard M
Signal and Image Processing Institute, University of Southern California, Los Angeles, CA.
Laboratory of Neuro Imaging, University of California, Los Angeles, CA.
Biomed Image Regist Proc. 2012 Jul;7359:180-189. doi: 10.1007/978-3-642-31340-0_19.
Registration and delineation of anatomical features in MRI of the human brain play an important role in the investigation of brain development and disease. Accurate, automatic and computationally efficient cortical surface registration and delineation of surface-based landmarks, including regions of interest (ROIs) and sulcal curves (sulci), remain challenging problems due to substantial variation in the shapes of these features across populations. We present a method that performs a fast and accurate registration, labeling and sulcal delineation of brain images. The new method presented in this paper uses a multiresolution, curvature based approach to perform a registration of a subject brain surface model to a delineated atlas surface model; the atlas ROIs and sulcal curves are then mapped to the subject brain surface. A geodesic curvature flow on the cortical surface is then used to refine the locations of the sulcal curves sulci and label boundaries further, such that they follow the true sulcal fundi more closely. The flow is formulated using a level set based method on the cortical surface, which represents the curves as zero level sets. We also incorporate a curvature based weighting that drives the curves to the bottoms of the sulcal valleys in the cortical folds. Finally, we validate our new approach by comparing sets of automatically delineated sulcal curves it produced to corresponding sets of manually delineated sulcal curves. Our results indicate that the proposed method is able to find these landmarks accurately.
人脑MRI中解剖特征的配准和描绘在脑发育和疾病研究中起着重要作用。由于这些特征在不同人群中的形状存在很大差异，准确、自动且计算高效的基于表面的地标（包括感兴趣区域（ROI）和脑沟曲线（脑沟））的皮质表面配准和描绘仍然是具有挑战性的问题。我们提出了一种对脑图像进行快速准确的配准、标记和脑沟描绘的方法。本文提出的新方法使用基于多分辨率、曲率的方法将受试者脑表面模型配准到描绘的图谱表面模型；然后将图谱ROI和脑沟曲线映射到受试者脑表面。接着，在皮质表面使用测地线曲率流进一步细化脑沟曲线（脑沟）的位置和标记边界，使其更紧密地跟随真实的脑沟底部。该流是基于皮质表面的水平集方法制定的，将曲线表示为零水平集。我们还纳入了基于曲率的加权，将曲线驱动到皮质褶皱中脑沟谷的底部。最后，我们通过将该方法自动描绘的脑沟曲线集与相应的手动描绘的脑沟曲线集进行比较，验证了我们的新方法。我们的结果表明，所提出的方法能够准确找到这些地标。
