Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Netherlands.
Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Netherlands.
Med Image Anal. 2017 Feb;36:216-228. doi: 10.1016/j.media.2016.12.002. Epub 2016 Dec 13.
A robust and accurate method is presented for the segmentation of the cranial cavity in computed tomography (CT) and CT perfusion (CTP) images. The method consists of multi-atlas registration with label fusion followed by a geodesic active contour levelset refinement of the segmentation. Pre-registration atlas selection based on differences in anterior skull anatomy reduces computation time whilst optimising performance. The method was evaluated on a large clinical dataset of 573 acute stroke and trauma patients that received a CT or CTP in our hospital in the period February 2015-December 2015. The database covers a large spectrum of the anatomical and pathological variations that is typically observed in everyday clinical practice. Three orthogonal slices were randomly selected per patient and manually annotated, resulting in 1659 reference annotations. Segmentations were initially visually inspected for the entire study cohort to assess failures. A total of 20 failures were reported. Quantitative evaluation in comparison to the reference dataset showed a mean Dice coefficient of 98.36 ± 2.59%. The results demonstrate that the method closely approaches the high performance of expert manual annotation.
本文提出了一种稳健且精确的颅脑 CT 和 CT 灌注（CTP）图像分割方法。该方法由多图谱配准和标签融合组成，随后进行基于测地线主动轮廓水平集细化分割。基于前颅解剖差异的预配准图谱选择可以减少计算时间，同时优化性能。该方法在我们医院于 2015 年 2 月至 2015 年 12 月期间接收的 573 例急性中风和创伤患者的大型临床数据集上进行了评估。该数据库涵盖了在日常临床实践中常见的解剖和病理变化的大谱。每位患者随机选择三个正交切片，并手动标注，共获得 1659 个参考标注。最初对整个研究队列进行了视觉检查以评估失败情况。报告了总共 20 次失败。与参考数据集的定量评估显示，平均 Dice 系数为 98.36 ± 2.59%。结果表明，该方法非常接近专家手动标注的高性能。
