Prastawa Marcel, Bullitt Elizabeth, Moon Nathan, Van Leemput Koen, Gerig Guido
Department of Computer Science, CB #3175, Sitterson Hall, Chapel Hill, NC 27599, USA.
Acad Radiol. 2003 Dec;10(12):1341-8. doi: 10.1016/s1076-6332(03)00506-3.
Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. An automated system has been developed for brain tumor segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments white matter, grey matter, cerebrospinal fluid, and edema. The segmentation of pathology and healthy structures is crucial for surgical planning and intervention.
The method performs the segmentation of a registered set of magnetic resonance images using an expectation-maximization scheme. The segmentation is guided by a spatial probabilistic atlas that contains expert prior knowledge about brain structures. This atlas is modified with the subject-specific brain tumor prior that is computed based on contrast enhancement.
Five cases with different types of tumors are selected for evaluation. The results obtained from the automatic segmentation program are compared with results from manual and semi-automated methods. The automated method yields results that have surface distances at roughly 1-4 mm compared with the manual results.
The automated method can be applied to different types of tumors. Although its performance is below that of the semi-automated method, it has the advantage of requiring no user supervision.
从磁共振图像中手动分割脑肿瘤是一项具有挑战性且耗时的任务。已开发出一种用于脑肿瘤分割的自动化系统，该系统将提供接近手动分割结果的客观、可重复的分割。此外，该方法还能分割白质、灰质、脑脊液和水肿。病理结构和健康结构的分割对于手术规划和干预至关重要。
该方法使用期望最大化方案对一组配准的磁共振图像进行分割。分割由一个空间概率图谱引导，该图谱包含有关脑结构的专家先验知识。此图谱会根据基于对比度增强计算出的特定个体脑肿瘤先验进行修改。
选择5例不同类型肿瘤的病例进行评估。将自动分割程序得到的结果与手动和半自动方法的结果进行比较。与手动结果相比，自动方法得到的结果表面距离约为1 - 4毫米。
该自动方法可应用于不同类型的肿瘤。虽然其性能低于半自动方法，但具有无需用户监督的优点。
