Biomedical Imaging Lab, Singapore Bioimaging Consortium, 30 Bioplolis Street #07-01, Matrix, Singapore.
Acad Radiol. 2010 Jun;17(6):718-26. doi: 10.1016/j.acra.2010.02.013.
Accurate segmentation of the brain ventricular system on computed tomographic (CT) imaging is useful in neurodiagnosis and neurosurgery. Manual segmentation is time consuming, usually not reproducible, and subjective. Because of image noise, low contrast between soft tissues, large interslice distance, large shape, and size variations of the ventricular system, no automatic method is presently available. The authors propose a model-guided method for the automated segmentation of the ventricular system.
Fifty CT scans of patients with strokes at different sites were collected for this study. Given a brain CT image, its ventricular system was segmented in five steps: (1) a predefined volumetric model was registered (or deformed) onto the image; (2) according to the deformed model, eight regions of interest were automatically specified; (3) the intensity threshold of cerebrospinal fluid was calculated in a region of interest and used to segment all regions of cerebrospinal fluid from the entire brain volume; (4) each ventricle was segmented in its specified region of interest; and (5) intraventricular calcification regions were identified to refine the ventricular segmentation.
Compared to ground truths provided by experts, the segmentation results of this method achieved an average overlap ratio of 85% for the entire ventricular system. On a desktop personal computer with a dual-core central processing unit running at 2.13 GHz, about 10 seconds were required to analyze each data set.
Experiments with clinical CT images showed that the proposed method can generate acceptable results in the presence of image noise, large shape, and size variations of the ventricular system, and therefore it is potentially useful for the quantitative interpretation of CT images in neurodiagnosis and neurosurgery.
准确分割脑 CT 影像中的脑室系统对于神经诊断和神经外科具有重要意义。手动分割费时费力，通常不可重复，且具有主观性。由于图像噪声、软组织对比度低、层间距大、脑室系统形状和大小变化大，目前还没有自动分割方法。本文提出了一种基于模型的方法，用于自动分割脑室系统。
本研究共采集了 50 例不同部位脑卒中患者的 CT 扫描。对于给定的脑 CT 图像，其脑室系统分割分 5 步进行：（1）将预定义的容积模型注册（或变形）到图像上；（2）根据变形后的模型，自动指定 8 个感兴趣区；（3）在感兴趣区内计算脑脊液的强度阈值，并用于从整个脑体积中分割所有脑脊液区域；（4）在指定的感兴趣区内分割每个脑室；（5）识别脑室内钙化区域以细化脑室分割。
与专家提供的真实值相比，该方法的分割结果在整个脑室系统上的平均重叠率为 85%。在一台具有双核中央处理器、运行频率为 2.13GHz 的台式个人计算机上，每个数据集的分析时间约为 10 秒。
临床 CT 图像实验表明，在存在图像噪声、脑室系统形状和大小变化较大的情况下，该方法可以产生可接受的结果，因此可能有助于神经诊断和神经外科中 CT 图像的定量解读。
