BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans.

Timothy J Hendrickson, Paul Reiners, Lucille A Moore, Jacob T Lundquist, Begim Fayzullobekova, Anders J Perrone, Erik G Lee, Julia Moser, Trevor K M Day, Dimitrios Alexopoulos, Martin Styner, Omid Kardan, Taylor A Chamberlain, Anurima Mummaneni, Henrique A Caldas, Brad Bower, Sally Stoyell, Tabitha Martin, Sooyeon Sung, Ermias Fair, Kenevan Carter, Jonathan Uriarte-Lopez, Amanda R Rueter, Essa Yacoub, Monica D Rosenberg, Christopher D Smyser, Jed T Elison, Alice Graham, Damien A Fair, Eric Feczko
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Abstract

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations.

Experimental design: Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance.

Principal observations: Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better.

Conclusions: BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

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BIBSNet:一个用于MRI扫描的深度学习婴儿图像大脑分割网络。
目的:婴儿磁共振(MR)图像的大脑分割在研究发育性心理健康和疾病方面至关重要。婴儿大脑在出生后的头几年经历了许多变化,这使得大多数现有算法难以进行组织分割。在这里,我们介绍了一个深度神经网络BIBSNet(婴儿和婴儿大脑分割神经网络),这是一个开源的社区驱动模型,依赖于数据增强和大量手动注释图像的样本量,以促进生成稳健和可推广的大脑分割。实验设计:模型训练和测试包括84名年龄在0-8个月(月经后中位年龄为13.57个月)的参与者的MR大脑图像。使用手动注释的真实和合成分割图像,使用10倍交叉验证程序对模型进行训练。使用金标准手动注释、联合标签融合(JLF)和BIBSNet生成的分割,对DCAN实验室婴儿ABCD BIDS处理管道处理的MRI数据进行测试,以评估模型性能。主要观察结果:使用组分析,结果表明使用BIBSNet分割产生的皮层指标优于JLF分割。此外,在分析个体差异时,BIBSNet分割的表现甚至更好。结论:在所分析的所有年龄组中,BIBSNet分割比JLF分割显示出显著的改进。与JLF相比,BIBSNet模型的速度快了600倍,并且可以很容易地包含在其他处理管道中。
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