Contusion segmentation from subjects with Traumatic Brain Injury: A random forest framework
Contusion segmentation from subjects with Traumatic Brain Injury: A random forest framework
复制标题
创伤性脑损伤受试者的挫伤分割：随机森林框架
DOI：
10.1109/isbi.2014.6867876
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
D. Rueckert
中科院分区：
文献类型：
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作者：
A. Rao;C. Ledig;V. Newcombe;D. Menon;D. Rueckert
外伤性脑损伤（Traumatic Brain Injury， TBI）是指突然的损伤导致大脑受到创伤。挫伤是TBI后最常见的病变类型之一，可以在受试者的MRI或CT上观察到。由于假设诸如挫伤负荷等指标可能是TBI的潜在生物标志物，因此分割挫伤的能力是非常可取的。目前，我们还没有发现任何完全自动化的方法来解决这个分割任务。在本文中，我们提出了一种完全自动化的基于随机森林的方法，使用多模态MRI进行挫伤分割。给定一组MR图像和ground-truth分割，为每个体素导出一组特征，这些特征既描述图像中的局部邻域信息，也描述图像中的远程上下文信息。使用这些特征和ground-truth体素标签训练随机森林，并用于对未见的测试对象进行自动混淆分割。我们在由23名受试者组成的数据集上使用6倍交叉验证来评估该方法，获得平均DICE重叠0.60。
Traumatic Brain Injury (TBI) occurs when a sudden injury causes trauma to the brain. Contusions are one of the most common types of lesion that arise after TBI, and they can be observed on a subject's MRI or CT. Since it is hypothesised that indices such as contusion load may be potential biomarkers for TBI, the ability to segment contusions is highly desirable. Currently, we are not aware of any fully automated methods that address this segmentation task. In this paper we present a completely automated random-forest based approach to contusion segmentation that uses multi-modality MRI. Given a training set of MR images and ground-truth segmentations, a set of features is derived for each voxel that describe both the local neighbourhood and longer-range contextual information in the images. A random forest is trained using these features and the ground-truth voxel labels, and used to produce an automatic contusion segmentation of an unseen test subject. We evaluate the method using 6-fold cross-validation on a dataset consisting of 23 subjects, obtaining a mean DICE overlap of 0.60.