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The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction
Medical Image Analysis ( IF 10.7 ) Pub Date : 2024-11-26 , DOI: 10.1016/j.media.2024.103394
Qiang Ma, Kaili Liang, Liu Li, Saga Masui, Yourong Guo, Chiara Nosarti, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert

The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain magnetic resonance (MR) images for the dHCP neonatal dataset. However, the current implementation of the pipeline requires more than 6.5 h to process a single MRI scan, making it expensive for large-scale neuroimaging studies. In this paper, we propose a fast deep learning (DL) based pipeline for dHCP neonatal cortical surface reconstruction, incorporating DL-based brain extraction, cortical surface reconstruction and spherical projection, as well as GPU-accelerated cortical surface inflation and cortical feature estimation. We introduce a multiscale deformation network to learn diffeomorphic cortical surface reconstruction end-to-end from T2-weighted brain MRI. A fast unsupervised spherical mapping approach is integrated to minimize metric distortions between cortical surfaces and projected spheres. The entire workflow of our DL-based dHCP pipeline completes within only 24 s on a modern GPU, which is nearly 1000 times faster than the original dHCP pipeline. The qualitative assessment demonstrates that for 82.5% of the test samples, the cortical surfaces reconstructed by our DL-based pipeline achieve superior (54.2%) or equal (28.3%) surface quality compared to the original dHCP pipeline.

中文翻译:


开发人类连接组项目:一种基于深度学习的快速新生儿皮质表面重建管道



开发人类连接组计划 (dHCP) 旨在探索围产期人脑的发育模式。已经开发了一种自动化处理管道,可以从结构性脑磁共振 (MR) 图像中提取高质量的皮质表面,用于 dHCP 新生儿数据集。然而,当前管道的实施需要超过 6.5 小时来处理一次 MRI 扫描,这使得大规模神经影像学研究的成本很高。在本文中,我们提出了一种基于深度学习 (DL) 的快速管道,用于 dHCP 新生儿皮质表面重建,结合了基于 DL 的大脑提取、皮层表面重建和球形投影,以及 GPU 加速的皮质表面充气和皮质特征估计。我们引入了一个多尺度变形网络,从 T2 加权脑部 MRI 中端到端地学习差异异形皮质表面重建。集成了一种快速无监督球面映射方法,以最大限度地减少皮质表面和投影球体之间的度量失真。我们基于 DL 的 dHCP 管道的整个工作流程在现代 GPU 上仅需 24 秒即可完成,这比原来的 dHCP 管道快了近 1000 倍。定性评估表明,对于 82.5% 的测试样品,与原始 dHCP 管道相比,我们基于 DL 的管道重建的皮质表面实现了优于 (54.2%) 或同等 (28.3%) 的表面质量。
更新日期:2024-11-26
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