Pei Yuchen, Chen Liangjun, Zhao Fenqiang, Wu Zhengwang, Zhong Tao, Wang Ya, Chen Changan, Wang Li, Zhang He, Wang Lisheng, Li Gang
Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2021 Sep-Oct;12907:239-248. doi: 10.1007/978-3-030-87234-2_23. Epub 2021 Sep 21.
Brain atlases are of fundamental importance for analyzing the dynamic neurodevelopment in fetal brain studies. Since the brain size, shape, and anatomical structures change rapidly during the prenatal period, it is essential to construct a spatiotemporal (4D) atlas equipped with tissue probability maps, which can preserve sharper early brain folding patterns for accurately characterizing dynamic changes in fetal brains and provide tissue prior informations for related tasks, e.g., segmentation, registration, and parcellation. In this work, we propose a novel unsupervised age-conditional learning framework to build temporally continuous fetal brain atlases by incorporating tissue segmentation maps, which outperforms previous traditional atlas construction methods in three aspects. , our framework enables learning age-conditional deformable templates by leveraging the entire collection. , we leverage reliable brain tissue segmentation maps in addition to the low-contrast noisy intensity images to enhance the alignment of individual images. , a novel loss function is designed to enforce the similarity between the learned tissue probability map on the atlas and each subject tissue segmentation map after registration, thereby providing extra anatomical consistency supervision for atlas building. Our 4D temporally- fetal brain atlases are constructed based on 82 healthy fetuses from 22 to 32 gestational weeks. Compared with the atlases built by the state-of-the-art algorithms, our atlases preserve more structural details and sharper folding patterns. Together with the learned tissue probability maps, our 4D fetal atlases provide a valuable reference for spatial normalization and analysis of fetal brain development.
脑图谱对于分析胎儿脑研究中的动态神经发育至关重要。由于在产前阶段脑的大小、形状和解剖结构变化迅速，构建配备组织概率图的时空（4D）图谱至关重要，该图谱可以保留更清晰的早期脑折叠模式，以准确表征胎儿脑的动态变化，并为相关任务（例如分割、配准和脑区划分）提供组织先验信息。在这项工作中，我们提出了一种新颖的无监督年龄条件学习框架，通过纳入组织分割图来构建时间上连续的胎儿脑图谱，该框架在三个方面优于以前的传统图谱构建方法。首先，我们的框架能够通过利用整个数据集来学习年龄条件可变形模板。其次，除了低对比度的噪声强度图像外，我们还利用可靠的脑组织分割图来增强个体图像的对齐。最后，设计了一种新颖的损失函数，以强制使图谱上学习到的组织概率图与配准后每个受试者的组织分割图之间具有相似性，从而为图谱构建提供额外的解剖一致性监督。我们的4D时间连续胎儿脑图谱是基于82例孕22至32周的健康胎儿构建的。与由最先进算法构建的图谱相比，我们的图谱保留了更多的结构细节和更清晰的折叠模式。连同学习到的组织概率图一起，我们的4D胎儿图谱为胎儿脑发育的空间归一化和分析提供了有价值的参考。
