School of Mathematics Physics and Information, Shaoxing University, Shaoxing, Zhejiang, 312000, China.
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China.
Sci Rep. 2019 Nov 14;9(1):16839. doi: 10.1038/s41598-019-53387-9.
Automatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies. To improve the hippocampus segmentation performance, a local binary pattern based feature extraction method is developed for machine learning based multi-atlas hippocampus segmentation. Under the framework of multi-atlas image segmentation (MAIS), a set of selected atlases are registered to images to be segmented using a non-linear image registration algorithm. The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted using a novel image feature extraction method. The RLBP based MAIS algorithm has been validated for segmenting hippocampus based on a data set of 135 T1 MR images which are from the Alzheimer's Disease Neuroimaging Initiative database (adni.loni.usc.edu). By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. We further computed Cohen's d effect size to investigate the sensitivity of each segmenting method in detecting volumetric differences of the hippocampus between different groups of subjects. The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. Hippocampal volumetric analysis showed that the proposed RLBP method performed well in detecting the volumetric differences of the hippocampus between groups of Alzheimer's disease patients, mild cognitive impairment subjects, and normal controls. These results have demonstrated that the RLBP based multi-atlas image segmentation method could facilitate efficient and accurate extraction of the hippocampus and may help predict Alzheimer's disease. The codes of the proposed method is available (https://www.nitrc.org/frs/?group_id=1242).
从磁共振（MR）脑图像中自动、可靠地分割出海马体在各种神经影像学研究中非常重要。为了提高海马体分割性能，本文提出了一种基于局部二值模式的特征提取方法，用于基于机器学习的多图谱海马体分割。在多图谱图像分割（MAIS）框架下，使用非线性图像配准算法将一组选择的图谱配准到待分割的图像中。然后，将配准的图谱用作训练数据，以基于使用新颖的图像特征提取方法提取的图像特征（称为随机局部二值模式（RLBP））构建用于分割图像的线性回归模型。基于来自阿尔茨海默病神经影像学倡议数据库（adni.loni.usc.edu）的 135 个 T1 MR 图像数据集，验证了基于 RLBP 的 MAIS 算法对海马体的分割。通过使用经验丰富的追踪者生成的手动分割标签作为真理标准，通过将自动分割结果与手动分割标签进行比较，使用六个分割评估指标来评估图像分割结果。我们进一步计算了 Cohen's d 效应量，以研究每种分割方法在检测不同组受试者海马体体积差异方面的敏感性。评估结果表明，在准确性方面，我们的方法与最先进的标签融合方法具有竞争力。海马体体积分析表明，所提出的 RLBP 方法在检测阿尔茨海默病患者、轻度认知障碍患者和正常对照组之间的海马体体积差异方面表现良好。这些结果表明，基于 RLBP 的多图谱图像分割方法可以促进海马体的高效、准确提取，并有助于预测阿尔茨海默病。该方法的代码可在（https://www.nitrc.org/frs/?group_id=1242）获得。
