Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images
Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images
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DOI：
10.3171/2014.9.peds12426
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发表时间：
2015-02-01
影响因子：
1.9
通讯作者：
Schiff, Steven J.
中科院分区：
文献类型：
--
作者：
Mandell, Jason G.;Langelaan, Jack W.;Schiff, Steven J.
在脑图像分析中，精确的边缘跟踪分割是一个尚未完全解决的问题。作者提出了一种新的算法，使用粒子滤波来跟踪大脑的边界，这种算法通常用于自主空中和地面车辆导航。他们的目标是创建一种多功能工具，用于在MRI和CT图像中分割大脑和液体，为智能自动化边缘跟踪器奠定基础，该边缘跟踪器独立于模态，并从MRI中分割可应用于MRI和CT的规范数据。方法利用模拟MRI数据集对粒子滤波分割算法进行训练和评价。然后应用该方法从美国国立卫生研究院儿科数据库的磁共振图像中生成0至18岁儿童和青少年的脑和液的规范生长曲线，并将这些数据与历史结果进行比较。作者进一步将这种方法应用于儿童脑积水的CT图像，并将结果与手工分割的数据进行了比较。结果对不同噪声水平（0%-9%）和空间不均匀性（0%-40%）的模拟MRI数据进行分割，导致脑容量的百分比误差在0.06% ~ 5.38%之间，液体体积的百分比误差在2.45% ~ 22.3%之间。作者使用该工具从MR图像中创建正常的脑和脑脊液生长曲线。计算的生长曲线与历史数据具有很好的一致性。此外，与人工分割相比，粒子滤波能准确分割5例脑积水儿童CT扫描的脑容量和液体体积（p < 0.001）。结论：作者首次建立了0-18岁儿童和青少年的标准脑和脑脊液生长曲线。此外，本研究还首次将粒子滤波作为图像分割的边缘跟踪器，并提供了一种半自动的方法从MR和CT图像中分割儿童和成人大脑数据。粒子过滤器具有进一步自动化的潜力，可用于临床而非研究工具。由于其方式独立，它有能力使CT成为一种更有效的神经系统疾病诊断工具，这在紧急情况下和CT往往是唯一可用的脑成像方法的发展中国家是一项非常重要的任务：
OBJECT Accurate edge tracing segmentation remains an incompletely solved problem in brain image analysis. The authors propose a novel algorithm using a particle filter to follow the boundary of the brain in the style often used in autonomous air and ground vehicle navigation. Their goals were to create a versatile tool to segment brain and fluid in MRI and CT images of the developing brain, lay the foundation far an intelligent automated edge tracker that is modality independent, and segment normative data from MRI that can be applied to both MRI and CT.METHODS Simulated MRI data sets were used to train and evaluate the particle filtersegmentation algorithm. The method was then applied to produce normative growth curves for children and adolescents from 0 to 18 years of age for brain and fluid from MR images from the National Institutes of Health pediatric database and these data were compared to historical results. The authors further adapted this method for use with CT images of pediatric hydrocephalus and compared the results with hand-segmented data.RESULTS Segmentation of simulated MRI data with varied levels of noise (0%-9%) and spatial inhomogeneity (0%-40%) resulted in percent errors ranging from 0.06% to 5.38% for brain volume and 2.45% to 22.3% for fluid volume. The authors used this tool to create normal brain and CSF growth curves from MR images. The calculated growth curves showed excellent consistency with historical data. Additionally, compared with manual segmentation the particle filter accurately segmented brain and fluid volumes from CT scans of 5 pediatric patients with hydrocephalus (p < 0.001).CONCLUSIONS The authors have produced the first normative brain and CSF growth curves for children and adolescents 0-18 years of age. In addition, this study includes the first use of a particle filter as an edge tracker in image segmentation and offers a semiautomatic method to segment both pediatric and adult brain data from MR and CT images. The particle filter has the potential to be further automated toward a clinical rather than research tool with both of these modalities. Because of its modality independence, it has the capability to allow CT to be a more effective diagnostic tool for neurological disorders, a task of substantial importance in emergency settings and in developing countries where CT is often the only available method of brain imaging: