Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.
Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.
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DOI：
10.1016/j.media.2014.04.003
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发表时间：
2014-07
影响因子：
10.9
通讯作者：
Torigian, Drew A.
中科院分区：
文献类型：
--
作者：
Udupa, Jayaram K.;Odhner, Dewey;Zhao, Liming;Tong, Yubing;Matsumoto, Monica M. S.;Ciesielski, Krzysztof C.;Falcao, Alexandre X.;Vaideeswaran, Pavithra;Ciesielski, Victoria;Saboury, Babak;Mohammadianrasanani, Syedmehrdad;Sin, Sanghun;Arens, Raanan;Torigian, Drew A.
为了使定量放射学（QR）在放射学实践中成为现实，计算机全身自动解剖识别（AAR）变得必不可少。为了建立一个与任何特定器官系统、身体区域或图像模态无关的通用AAR系统，本文提出了一种基于模糊建模思想的AAR方法，用于定位和描绘不同身体区域的所有主要器官，并将模糊模型与迭代相对模糊连通性（IRFC）描述算法紧密结合。该方法包括五个主要步骤：(a)收集图像数据，用于构建模型和测试我们卫生系统中现有的患者图像集的AAR算法；(b)制订每个身体区域和器官的精确定义，并按照这些定义划定它们；(c)建立各身体区域器官层次模糊解剖模型；(d)利用分层模型在给定图像中识别和定位器官；（五）按层次划分各机关。在步骤(c)中，我们显式地将对象大小和位置关系编码到层次结构中，随后在步骤(d)中的对象识别和步骤(e)中的描述中利用这些信息。模态独立方面和依赖方面在模型编码中被小心地分离。在模型构建阶段，进行了一个学习过程，以排练最优的基于阈值的目标识别方法。步骤(d)中的识别过程从大型的、定义良好的对象开始，并以全局到局部的方式沿着层次结构进行。通过将模糊模型约束自然地集成到描述算法中，创建了基于模糊模型的IRFC算法版本。AAR系统在三个身体区域进行测试-胸部（CT），腹部（CT和MRI）和颈部（MRI和CT） -涉及总共超过35个器官和130个数据集（用于模型构建和测试的总数）。除了颈部，训练和测试数据集在所有情况下都被划分为相等的大小。总体而言，AAR方法在定位非稀疏斑点状物体和大多数稀疏管状物体时，平均精度约为2体素。对于非稀疏对象，平均假阳性和假阴性体积分数的描述精度分别为2%和8%，对于稀疏对象，平均假阳性和假阴性体积分数的描述精度分别为5%和15%。两个目标组相对于地面真值的平均边界距离分别为0.9和1.5体素。一些稀疏的物体——静脉系统（CT显示在胸部）、下腔静脉（CT显示在腹部）、下颌骨和鼻咽部（MRI显示在颈部，但CT上没有）——在各个层面都构成了挑战，导致识别和/或描绘结果较差。与近期文献中肝脏、肾脏和脾脏CT图像的方法相比，AAR方法的效果相当好。我们得出的结论是，模态独立与依赖方面的分离、在层次结构中组织对象、将对象关系信息明确编码到层次结构中、基于最优阈值的识别学习和基于模糊模型的IRFC是有效的概念，这些概念使我们能够证明在不同器官和不同模态的不同身体区域中工作的通用AAR系统的可行性。
To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide automatic anatomy recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions – thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) – involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and testing data sets are divided into equal size in all cases except for the neck. Overall the AAR method achieves a mean accuracy of about 2 voxels in localizing non-sparse blob-like objects and most sparse tubular objects. The delineation accuracy in terms of mean false positive and negative volume fractions is 2% and 8%, respectively, for non-sparse objects, and 5% and 15%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 0.9 and 1.5 voxels, respectively. Some sparse objects – venous system (in the thorax on CT), inferior vena cava (in the abdomen on CT), and mandible and naso-pharynx (in neck on MRI, but not on CT) – pose challenges at all levels, leading to poor recognition and/or delineation results. The AAR method fares quite favorably when compared with methods from the recent literature for liver, kidneys, and spleen on CT images. We conclude that separation of modality-independent from dependent aspects, organization of objects in a hierarchy, encoding of object relationship information explicitly into the hierarchy, optimal threshold-based recognition learning, and fuzzy model-based IRFC are effective concepts which allowed us to demonstrate the feasibility of a general AAR system that works in different body regions on a variety of organs and on different modalities.
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影响因子：
3.8
作者：
Chen, Xinjian;Bagci, Ulas
通讯作者：
Bagci, Ulas
DOI：
10.1109/34.368173
发表时间：
1995-02-01
影响因子：
23.6
作者：
MALLADI, R;SETHIAN, JA;VEMURI, BC
通讯作者：
VEMURI, BC
DOI：
10.1164/rccm.201008-1249oc
发表时间：
2011-03-15
影响因子：
24.7
作者：
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通讯作者：
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影响因子：
10.9
作者：
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通讯作者：
Lelieveldt, Boudewijn P. F.
影响因子：
10.9
作者：
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通讯作者：
Siddiqui, K.