分割
计算机科学
人工智能
平滑度
约束(计算机辅助设计)
模式识别(心理学)
一致性(知识库)
图像分割
深度学习
计算机视觉
数学
几何学
数学分析
作者
Jie Wei,Feng Shi,Zhiming Cui,Yongsheng Pan,Yong Xia,Dinggang Shen
标识
DOI:10.1007/978-3-030-87193-2_9
摘要
Accurate and consistent segmentation of longitudinal brain magnetic resonance (MR) images is of great importance in studying brain morphological and functional changes over time. However, current available brain segmentation methods, especially deep learning methods, are mostly trained with cross-sectional brain images that might generate inconsistent results in longitudinal studies. To overcome this limitation, we present a novel coarse-to-fine spatio-temporal constrained deep learning model for consistent longitudinal segmentation based on limited labeled cross-sectional data with semi-supervised learning. Specifically, both segmentation smoothness and temporal consistency are imposed in the loss function. Moreover, brain structural changes over time are summarized as age constraint, to make the model better reflect the trends of longitudinal aging changes. We validate our proposed method on 53 sets of longitudinal T1-weighted brain MR images from ADNI, with an average of 4.5 time-points per subject. Both quantitative and qualitative comparisons with comparison methods demonstrate the superior performance of our proposed method.
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