Shape-based acetabular cartilage segmentation: application to CT and MRI datasets
Shape-based acetabular cartilage segmentation: application to CT and MRI datasets
复制标题
基于形状的髋臼软骨分割：在 CT 和 MRI 数据集上的应用
DOI：
10.1007/s11548-015-1313-z
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发表时间：
2016
影响因子：
3
通讯作者：
Sato Y
中科院分区：
文献类型：
--
作者：
Tabrizi PR;Zoroofi RA;Yokota F;Nishii T;Sato Y
PurposeA新方法髋臼软骨分割在计算机断层扫描（CT）关节造影和磁共振成像（MRI）数据集与腿部tensions.MethodsThe新的分割方法是基于形状和强度的信息相结合。根据U形髋臼区域与髋臼软骨之间的可预测非线性关系来获取形状信息。从髋臼软骨区域自动获得强度信息以完成分割过程。使用54个具有两种不同辐射剂量的CT关节造影数据集和20个MRI数据集对该方法进行评价。此外，该方法在识别髋臼软骨的性能进行了比较与其他四个髋臼软骨分割methods.ResultsThis方法优于比较方法。事实上，该方法对于74个数据集保持了良好的准确度水平，与软骨模态无关，并且在骨分割过程中具有最小的用户交互。此外，该方法是有效的，在嘈杂的条件下，并在零厚度，这证实了其潜在的临床usefuls.ConclusionsOur新方法提出髋臼软骨分割在三个不同的数据集的基础上相结合的形状和强度的信息。这种方法在髋臼和股骨软骨之间有明确边界的情况下执行得很好。然而，髋臼软骨和骨盆骨的信息应该从一个数据集，如CT关节造影或MRI数据集与腿部牵引。
PurposeA new method for acetabular cartilage segmentation in both computed tomography (CT) arthrography and magnetic resonance imaging (MRI) datasets with leg tension is developed and tested.MethodsThe new segmentation method is based on the combination of shape and intensity information. Shape information is acquired according to the predictable nonlinear relationship between the U-shaped acetabulum region and acetabular cartilage. Intensity information is obtained from the acetabular cartilage region automatically to complete the segmentation procedures. This method is evaluated using 54 CT arthrography datasets with two different radiation doses and 20 MRI datasets. Additionally, the performance of this method in identifying acetabular cartilage is compared with four other acetabular cartilage segmentation methods.ResultsThis method performed better than the comparison methods. Indeed, this method maintained good accuracy level for 74 datasets independent of the cartilage modality and with minimum user interaction in the bone segmentation procedures. In addition, this method was efficient in noisy conditions and in detection of the damaged cartilages with zero thickness, which confirmed its potential clinical usefulness.ConclusionsOur new method proposes acetabular cartilage segmentation in three different datasets based on the combination of the shape and intensity information. This method executes well in situations where there are clear boundaries between the acetabular and femoral cartilages. However, the acetabular cartilage and pelvic bone information should be obtained from one dataset such as CT arthrography or MRI datasets with leg traction.