Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures
Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures
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DOI：
10.1109/tmi.2011.2180920
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发表时间：
2012-03-01
影响因子：
10.6
通讯作者：
Udupa, Jayaram K.
中科院分区：
文献类型：
--
作者：
Bagci, Ulas;Chen, Xinjian;Udupa, Jayaram K.
从医学图像中分割解剖结构是一个具有挑战性的问题，这取决于在描绘之前对解剖结构的准确识别（定位）。该研究通过解决两个主要问题来概括解剖分割问题：1)在不进行搜索或优化的情况下自动定位解剖结构；2)基于定位的模型装配自动圈定解剖结构。对于1)，我们提出了强度加权球尺度目标提取概念，构建了从图像空间到物体（形状）空间的分层传递函数，从而无需搜索或优化即可识别三维医学图像中的解剖结构。对于2)，我们将GC分割算法与先验形状模型相结合。该综合分割框架在临床3-D图像上进行了评估，这些图像由一组20个腹部CT扫描组成。此外，我们使用一组11英尺的MR图像来测试我们的方法对不同成像模式的通用性以及所提出方法的鲁棒性和准确性。由于磁共振图像强度不具有组织特定的数值意义，我们还探讨了强度非标准化对解剖目标识别的影响。实验结果表明：1)有效的识别可以提高图像的描绘精度；2)通过模型装配将大量解剖结构纳入形状模型，显著提高了识别和圈定精度；3)球尺度产生关于物体和图像之间关系的有用信息；4)集合中场景之间的强度变化会降低目标识别性能。
Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1), we propose intensity weighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2), we integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition, we use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as well as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning, we also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship between the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance.