The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice’s index of \(0.88 \pm 0.01\) (\(0.87 \pm 0.01\)) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi.

中文翻译：

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使用随机欠采样和增强算法在 3D MRI 中自动分割海马。


磁共振成像中大脑结构的自动识别对于神经科学研究和可能的临床诊断工具都非常重要。在这项研究中，提出了一种在 MRI 中全自动海马分割的新策略。它基于名为 RUSBoost 的监督算法，该算法将数据随机欠采样与增强算法相结合。 RUSBoost 是一种专门为不平衡分类设计的算法，适用于大型数据集，因为它使用多数类的随机欠采样。 RUSBoost 的性能与 ADABoost、随机森林和公开的大脑分割包 FreeSurfer 的性能进行了比较。这项研究是在 50 张 T1 加权大脑结构图像的数据集上进行的。基于 RUSBoost 的分割工具取得了最佳结果，左（右）脑半球的 Dice 指数为\(0.88 \pm 0.01\) ( \(0.87 \pm 0.01\) )。使用 50 个 T1 加权结构脑扫描的独立数据集对经过充分训练的策略进行独立验证。 RUSBoost 分割再次优于手动分割，在四种工具中性能最高。此外，手动和 RUSBoost 分割计算的海马体积之间的皮尔逊相关系数（左（右）侧）为 0.83（0.82），具有统计显着性，并且高于 Adaboost、Random Forest 和 FreeSurfer 计算的结果。所提出的方法可能适用于海马的准确、稳健和统计显着性分割。