Hao Shijie, Li Gang, Wang Li, Meng Yu, Shen Dinggang
School of Computer and Information, Hefei University of Technology, Anhui, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9900:219-227. doi: 10.1007/978-3-319-46720-7_26. Epub 2016 Oct 2.
Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However, due to rapid growth and ongoing myelination, infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns, thus leading to much more topological errors (holes and handles) in the cortical surfaces derived from tissue segmentation results, in comparison to adult MR images which typically have good tissue contrast. Existing methods for topological correction either rely on the minimal correction criteria, or rules based on image intensity priori, thus often resulting in erroneous correction and large anatomical errors in reconstructed infant cortical surfaces. To address these issues, we propose to correct topological errors by learning information from the anatomical references, i.e., manually corrected images. Specifically, in our method, we first locate candidate voxels of topologically defected regions by using a topology-preserving level set method. Then, by leveraging rich information of the corresponding patches from reference images, we build dictionaries from the anatomical references and infer the correct labels of candidate voxels using sparse representation. Notably, we further integrate these two steps into an iterative framework to enable gradual correction of large topological errors, which are frequently occurred in infant images and cannot be completely corrected using one-shot sparse representation. Extensive experiments on infant cortical surfaces demonstrate that our method effectively corrects the topological defects, leads to better anatomical consistency, compared to the state-of-the-art methods.
从婴儿磁共振成像（MR）图像重建拓扑正确且准确的皮质表面，在早期脑发育的神经成像图谱研究中具有极其重要的意义。然而，由于婴儿脑快速生长且髓鞘化过程持续进行，婴儿MR图像呈现出极低的组织对比度和动态外观模式，因此与通常具有良好组织对比度的成人MR图像相比，由组织分割结果得到的皮质表面会出现更多的拓扑错误（空洞和手柄状结构）。现有的拓扑校正方法要么依赖于最小校正标准，要么基于图像强度先验规则，因此在重建的婴儿皮质表面常常导致错误的校正和较大的解剖学误差。为了解决这些问题，我们建议通过从解剖学参考（即手动校正的图像）中学习信息来校正拓扑错误。具体而言，在我们的方法中，我们首先使用一种保持拓扑结构的水平集方法定位拓扑缺陷区域的候选体素。然后，通过利用参考图像中相应斑块的丰富信息，我们从解剖学参考中构建字典，并使用稀疏表示推断候选体素的正确标签。值得注意的是，我们进一步将这两个步骤整合到一个迭代框架中，以实现对婴儿图像中经常出现的大拓扑错误的逐步校正，而这些错误无法通过一次性稀疏表示完全校正。在婴儿皮质表面上进行的大量实验表明，与现有最先进的方法相比，我们的方法能够有效地校正拓扑缺陷，实现更好的解剖学一致性。
