Zheng Hao, Chen Xiaoyang, Li Hongming, Chen Tingting, Liang Peixian, Fan Yong
Department of Radiology, The Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.
bioRxiv. 2024 Dec 10:2024.12.10.626888. doi: 10.1101/2024.12.10.626888.
Deep learning-based cortical surface reconstruction (CSR) methods heavily rely on pseudo ground truth (pGT) generated by conventional CSR pipelines as supervision, leading to dataset-specific challenges and lengthy training data preparation. We propose a new approach for reconstructing multiple cortical surfaces using from brain MRI ribbon segmentations. Our approach initializes a midthickness surface and then deforms it inward and outward to form the inner (white matter) and outer (pial) cortical surfaces, respectively, by jointly learning diffeomorphic flows to align the surfaces with the boundaries of the cortical ribbon segmentation maps. Specifically, a boundary surface loss drives the initialization surface to the target inner and outer boundaries, and an inter-surface normal consistency loss regularizes the pial surface in challenging deep cortical sulci. Additional regularization terms are utilized to enforce surface smoothness and topology. Evaluated on two large-scale brain MRI datasets, our weakly-supervised method achieves comparable or superior CSR accuracy and regularity to existing supervised deep learning alternatives.
基于深度学习的皮质表面重建（CSR）方法严重依赖于传统CSR管道生成的伪真值（pGT）作为监督，这导致了特定数据集的挑战和冗长的训练数据准备。我们提出了一种使用脑MRI带状分割来重建多个皮质表面的新方法。我们的方法初始化一个中间厚度表面，然后通过联合学习微分同胚流，使该表面分别向内和向外变形，以形成内（白质）皮质表面和外（软膜）皮质表面，从而使这些表面与皮质带状分割图的边界对齐。具体来说，边界表面损失将初始化表面驱动到目标内边界和外边界，表面间法线一致性损失在具有挑战性的深部皮质沟中对软膜表面进行正则化。还使用了额外的正则化项来增强表面的平滑度和拓扑结构。在两个大规模脑MRI数据集上进行评估时，我们的弱监督方法在CSR准确性和规则性方面达到了与现有监督深度学习方法相当或更优的水平。
