Brown M S, McNitt-Gray M F, Mankovich N J, Goldin J G, Hiller J, Wilson L S, Aberle D R
Department of Radiological Sciences, UCLA School of Medicine, Los Angeles, CA 90095-1721, USA.
IEEE Trans Med Imaging. 1997 Dec;16(6):828-39. doi: 10.1109/42.650879.
We present an automated, knowledge-based method for segmenting chest computed tomography (CT) datasets. Anatomical knowledge including expected volume, shape, relative position, and X-ray attenuation of organs provides feature constraints that guide the segmentation process. Knowledge is represented at a high level using an explicit anatomical model. The model is stored in a frame-based semantic network and anatomical variability is incorporated using fuzzy sets. A blackboard architecture permits the data representation and processing algorithms in the model domain to be independent of those in the image domain. Knowledge-constrained segmentation routines extract contiguous three-dimensional (3-D) sets of voxels, and their feature-space representations are posted on the blackboard. An inference engine uses fuzzy logic to match image to model objects based on the feature constraints. Strict separation of model and image domains allows for systematic extension of the knowledge base. In preliminary experiments, the method has been applied to a small number of thoracic CT datasets. Based on subjective visual assessment by experienced thoracic radiologists, basic anatomic structures such as the lungs, central tracheobronchial tree, chest wall, and mediastinum were successfully segmented. To demonstrate the extensibility of the system, knowledge was added to represent the more complex anatomy of lung lesions in contact with vessels or the chest wall. Visual inspection of these segmented lesions was also favorable. These preliminary results suggest that use of expert knowledge provides an increased level of automation compared with low-level segmentation techniques. Moreover, the knowledge-based approach may better discriminate between structures of similar attenuation and anatomic contiguity. Further validation is required.
我们提出了一种基于知识的胸部计算机断层扫描（CT）数据集自动分割方法。包括器官的预期体积、形状、相对位置和X射线衰减在内的解剖学知识提供了特征约束，可指导分割过程。知识通过显式解剖模型在高层进行表示。该模型存储在基于框架的语义网络中，并使用模糊集纳入解剖学变异性。黑板架构允许模型域中的数据表示和处理算法独立于图像域中的算法。知识约束分割例程提取连续的三维（3-D）体素集，并将其特征空间表示发布到黑板上。推理引擎使用模糊逻辑根据特征约束将图像与模型对象进行匹配。模型域和图像域的严格分离允许知识库的系统扩展。在初步实验中，该方法已应用于少量胸部CT数据集。根据经验丰富的胸部放射科医生的主观视觉评估，成功分割了肺、中央气管支气管树、胸壁和纵隔等基本解剖结构。为了证明该系统的可扩展性，添加了知识以表示与血管或胸壁接触的更复杂的肺部病变解剖结构。对这些分割病变的目视检查结果也令人满意。这些初步结果表明，与低级分割技术相比，使用专家知识可提高自动化程度。此外，基于知识的方法可能能更好地区分具有相似衰减和解剖连续性的结构。还需要进一步验证。
