Disorder Development Onset Prediction Based on Spatiotemporal Statistical Shape Model
Disorder Development Onset Prediction Based on Spatiotemporal Statistical Shape Model
复制标题
基于时空统计形状模型的疾病发展发病预测
DOI：
10.1109/smc.2018.00075
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Kobashi Syoji
中科院分区：
文献类型：
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作者：
Alam Saadia Binte;Shimizu Akinobu;Ando Kumiko;Ishikura Reiichi;Kobashi Syoji
在早期发育阶段，大脑在大小、形状和外观上的变化比生命中的任何其他阶段都要多。更好地了解大脑发育可以通过早期发现和应用补救教育来减少发育障碍的症状。在本文中，我们提出了一个计算机辅助诊断（CAD）系统，估计发病概率的脑发育障碍，使用新生儿脑MR图像。该CAD系统首先构建新生儿大脑的时空统计形状模型（stSSM），提取静态和动态形态特征，并利用机器学习技术估计概率。本文提出了stSSM构造方法，该方法通过扩展以前的基于EM的stSSM构造方法来产生时间连续的特征向量。该方法已被验证，将其应用于12个新生儿的大脑，其修订的年龄在-5至730天之间。
During the early developmental stage, the brain undergoes more changes in size, shape, and appearance than at any other stage in life. A better understanding of brain development can decrease the symptom of development disorder through very early detection and application of remedial education. In this paper, we present a computer-aided diagnosis (CAD) system, which estimates onset probability of brain development disorder using neonatal brain MR images. The CAD system first constructs spatiotemporal statistical shape model (stSSM) of neonatal brain, extracts static and dynamic morphological features, and estimates the probability using machine learning techniques. This paper proposes the stSSM construction method which produces temporally continuous Eigenvectors by extending previous EM-based-stSSM construction method. The method has been validated by applying it to 12 neonatal brains whose revised ages are between - 5 to 730 days.