Automatic anatomy recognition via multiobject oriented active shape models
Automatic anatomy recognition via multiobject oriented active shape models
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DOI：
10.1118/1.3515751
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发表时间：
2010-12-01
期刊：
影响因子：
3.8
通讯作者：
Torigian, Drew A.
中科院分区：
文献类型：
--
作者：
Chen, Xinjian;Udupa, Jayaram K.;Torigian, Drew A.
目的：本文研究了在临床放射学中开发一个自动解剖识别系统的可行性，并演示了其在临床2D图像上的操作。方法：本文描述的解剖识别方法包括两个主要部分：（a）OASM的多目标泛化和（B）目标识别策略。OASM算法通过为每个对象包括一个模型并按照火线的精神为每个对象指定一个特定的成本结构来推广到多个对象。在MOASM中通过三级动态规划算法完成多对象边界的描绘，其中第一级是在像素级，其目的是找到连续地标之间的最佳定向边界段，第二级是在地标级，其目的是找到地标的最佳位置，第三级是在对象级，其目的是找到所有对象上的对象边界的最佳布置。对象识别策略试图找到多对象模型的姿态向量（包括平移、旋转和缩放分量），该姿态向量为所有对象产生最小的总边界成本。利用常规临床胸部CT、腹部CT和足部MRI数据集分别评价了描绘和识别准确性。根据真阳性和假阳性体积分数（TPVF和FPVF）评价描绘准确性。（1）根据产生高描绘准确度的模型组件的姿势向量的空间大小，（2）作为模型中对象的数量和对象的分布和大小的函数，（3）根据描绘和识别之间的相互依赖性，（4）最优识别结果与全局最优识别结果的接近程度。当模型中包含多个对象时，TPVF的描绘精度可以提高到97%-98%，而FPVF为0.1%-0.2%。通常，识别准确度>= 90%产生TPVF >= 95%和FPVF
Purpose: This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images.Methods: The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum.Results: When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a recognition accuracy of >= 90% yielded a TPVF >= 95% and FPVF