Pereira Sérgio, Pinto Adriano, Oliveira Jorge, Mendrik Adriënne M, Correia José H, Silva Carlos A
CMEMS-UMinho Research Unit, University of Minho, Campus Azurém, Guimarães, Portugal.
CMEMS-UMinho Research Unit, University of Minho, Campus Azurém, Guimarães, Portugal.
J Neurosci Methods. 2016 Sep 1;270:111-123. doi: 10.1016/j.jneumeth.2016.06.017. Epub 2016 Jun 18.
The segmentation of brain tissue into cerebrospinal fluid, gray matter, and white matter in magnetic resonance imaging scans is an important procedure to extract regions of interest for quantitative analysis and disease assessment. Manual segmentation requires skilled experts, being a laborious and time-consuming task; therefore, reliable and robust automatic segmentation methods are necessary.
We propose a segmentation framework based on a Conditional Random Field for brain tissue segmentation, with a Random Forest encoding the likelihood function. The features include intensities, gradients, probability maps, and locations. Additionally, skull stripping is critical for achieving an accurate segmentation; thus, after extracting the brain we propose to refine its boundary during segmentation.
The proposed framework was evaluated on the MR Brain Image Segmentation Challenge and the Internet Brain Segmentation Repository databases. The segmentations of brain tissues obtained with the proposed algorithm were competitive both in normal and diseased subjects. The skull stripping refinement significantly improved the results, when comparing against no refinement.
In the MR Brain Image Segmentation Challenge database, the results were competitive when comparing with top methods. In the Internet Brain Segmentation Repository database, the proposed approach outperformed other well-established algorithms.
The combination of a Random Forest and Conditional Random Field for brain tissue segmentation performed well for normal and diseased subjects. Additionally, refinement of the skull stripping at segmentation time is feasible in learning-based methods and significantly improves the segmentation of cerebrospinal fluid and intracranial volume.
在磁共振成像扫描中将脑组织分割为脑脊液、灰质和白质是提取感兴趣区域以进行定量分析和疾病评估的重要步骤。手动分割需要技术熟练的专家，是一项费力且耗时的任务；因此，可靠且强大的自动分割方法是必要的。
我们提出了一种基于条件随机场的脑组织分割框架，使用随机森林对似然函数进行编码。特征包括强度、梯度、概率图和位置。此外，去除颅骨对于实现准确分割至关重要；因此，在提取脑部后，我们建议在分割过程中细化其边界。
所提出的框架在MR脑图像分割挑战赛和互联网脑分割库数据库上进行了评估。所提算法获得的脑组织分割在正常和患病受试者中均具有竞争力。与未细化相比，去除颅骨细化显著改善了结果。
在MR脑图像分割挑战赛数据库中，与顶级方法相比，结果具有竞争力。在互联网脑分割库数据库中，所提方法优于其他成熟算法。
用于脑组织分割的随机森林和条件随机场的组合在正常和患病受试者中表现良好。此外，在分割时对去除颅骨进行细化在基于学习的方法中是可行的，并且显著改善了脑脊液和颅内体积的分割。
