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首页> 外文期刊>BMC Medical Informatics and Decision Making >Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching
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Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

机译：通过组合低层分割和高层模板匹配，自动在脑部CT图像中进行心室系统分割

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BackgroundAccurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information.MethodsFirst all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases.ResultsExperiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images.ConclusionThe experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.
机译：背景技术CT脑部扫描的准确分析对于创伤性脑损伤（TBI）的诊断和治疗至关重要。这些CT脑部扫描的自动处理可以加快决策过程，降低医疗保健成本，并减少人为错误的可能性。在本文中，我们专注于CT脑部图像的自动处理以分割和识别心室系统。脑室的分割为形成重要诊断信息的脑室变化提供了定量的测量方法。方法首先，通过检测所有图像中的理想中线来对齐所有CT切片。基于头骨对称性找到理想的大脑中线的初始估计，然后使用检测到的解剖特征进一步完善初始估计。然后采用两步法进行心室分割。首先，将每个像素的低级分割应用于CT图像。对于此步骤，将评估并比较迭代条件模式（ICM）和最大后验空间概率（MASP）。第二步应用模板匹配算法将初始低级分割中的对象识别为心室。使用相对较大的包含轻度和重度TBI病例的CT数据集进行心室分割实验，结果实验表明理想的中线检测可接受率超过95％。定义了两次测量以评估心室识别结果。第一个量度是类似灵敏度的量度，第二个量度是像假阳性的量度。对于第一次测量，该比率为100％，表明在所有切片中均识别出所有心室。假阳性样测量值为8.59％。通过在CT图像上的数学关系和分割结果，我们也指出了ICM和MASP算法之间的异同。结论实验证明了所提算法的可靠性。所提出的方法的新颖性在于它结合了用于理想中线检测的解剖特征和两步心室分割方法。与现有方法相比，我们的方法具有以下改进：使用解剖特征和从磁共振图像得出的空间模板对理想中线进行准确检测，并对心室进行准确识别。

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