Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America.
PLoS One. 2010 Nov 8;5(11):e13874. doi: 10.1371/journal.pone.0013874.
Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods. We describe our efforts to develop such methods in high risk newborns using a combination of manual and automated segmentation tools. After intensive efforts to accurately define structural boundaries, two trained raters independently performed manual segmentation of nine subcortical structures using axial T2-weighted MRI scans from 20 randomly selected extremely preterm infants. All scans were re-segmented by both raters to assess reliability. High intra-rater reliability was achieved, as assessed by repeatability and intra-class correlation coefficients (ICC range: 0.97 to 0.99) for all manually segmented regions. Inter-rater reliability was slightly lower (ICC range: 0.93 to 0.99). A semi-automated segmentation approach was developed that combined the parametric strengths of the Hidden Markov Random Field Expectation Maximization algorithm with non-parametric Parzen window classifier resulting in accurate white matter, gray matter, and CSF segmentation. Final manual correction of misclassification errors improved accuracy (similarity index range: 0.87 to 0.89) and facilitated objective quantification of white matter signal abnormalities. The semi-automated and manual methods were seamlessly integrated to generate full brain segmentation within two hours. This comprehensive approach can facilitate the evaluation of large cohorts to rigorously evaluate the utility of regional brain volumes as biomarkers of neonatal care and surrogate endpoints for neurodevelopmental outcomes.
大多数极早产儿在胎龄相当时表现出大脑萎缩/生长障碍和 MRI 上的白质信号异常。MRI 脑容量可作为评估新生儿重症监护效果和预测神经发育结果的生物标志物。这需要详细、准确和可靠的脑 MRI 分割方法。我们描述了使用手动和自动分割工具相结合的方法，在高危新生儿中开发这种方法的努力。在努力准确定义结构边界之后，两名经过培训的评估者使用 20 名随机选择的极早产儿的轴向 T2 加权 MRI 扫描，独立地对 9 个皮质下结构进行手动分割。对所有扫描进行了重新分割，由两名评估者评估可靠性。通过可重复性和组内相关系数（ICC 范围：0.97 至 0.99）评估，所有手动分割区域均达到了高的组内可靠性。组间可靠性略低（ICC 范围：0.93 至 0.99）。开发了一种半自动分割方法，该方法结合了隐马尔可夫随机场期望最大化算法的参数优势和非参数 Parzen 窗口分类器，从而实现了准确的白质、灰质和 CSF 分割。对错误分类错误的最终手动校正提高了准确性（相似指数范围：0.87 至 0.89），并促进了对白质信号异常的客观量化。半自动和手动方法无缝集成，在两小时内生成全脑分割。这种综合方法可以促进对大队列的评估，以严格评估区域脑容量作为新生儿护理的生物标志物和神经发育结果的替代终点。
