3D automatic anatomy segmentation based on iterative graph-cut-ASM
3D automatic anatomy segmentation based on iterative graph-cut-ASM
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DOI：
10.1118/1.3602070
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发表时间：
2011-08-01
期刊：
影响因子：
3.8
通讯作者：
Bagci, Ulas
中科院分区：
文献类型：
--
作者：
Chen, Xinjian;Bagci, Ulas
目的：本文研究了在临床放射学中开发一个自动解剖分割系统的可行性，并演示了它在临床三维图像上的操作。方法：作者正在开发的自动解剖分割系统包括两个主要部分：目标识别和目标描绘。至于识别，分层的三维尺度为基础的多目标方法用于多目标识别任务，它将强度加权球标度（b尺度）的信息到主动形状模型（ASM）。对于物体轮廓，提出了一种迭代图切割ASM（IGCASM）算法，该算法有效地结合了ASM中包含的丰富的统计形状信息和GC方法的全局最优轮廓能力。所提出的IGCASM算法是他们先前在Chen等人[Proc. SPIE，7259，72590 C1 - 72590 C-8（2009）]中提出的2D GC-ASM方法的3D推广。所提出的方法进行了测试，包括从20例（10名男性和10名女性）的临床腹部CT扫描，和11足磁共振成像（MRI）扫描图像的两个数据集。该测试针对四个器官（肝脏、左右肾脏和脾脏）分割，五个足骨（跟骨、胫骨、骰骨、距骨和舟骨）。识别和描绘精度分别进行了评估。在平移、旋转和尺度（尺寸）误差方面评估识别准确度。根据真阳性和假阳性体积分数（TPVF，FPVF）评价描绘准确性。结果：在3.4GHZ CPU的Intel Pentium IV PC机上，对所有器官的平移、旋转和尺度误差的识别精度分别为8 mm、10 °和0.03，对所有足部骨骼的识别精度分别为3.5709mm、0.35 °和0.025。以TPVF和FPVF表示的所有受试者所有器官的描绘准确度分别为93.01%和0.22%，所有受试者的所有足部骨骼分别为93.75%和0.28%。虽然四个器官的描绘可以相当迅速地完成，平均为78秒，五个足骨的描绘可以完成平均为70秒。结论：实验结果表明，所提出的自动解剖分割系统的可行性和有效性：（a）形状先验的GC框架中的合并是可行的，在3D中，如先前所证明的2D图像;（B）我们在3D中的结果证实了在2D中观察到的准确性行为。混合策略IGCASM似乎比单独的ASM和GC更稳健和准确;和（c）具有临床重要性的身体区域和足部骨骼内的描绘可以在1.5分钟内相当迅速地完成。（C）2011年美国医学物理学家协会。[DOI：10.1118/1.3602070]
Purpose: This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images.Methods: The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine.Results: The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s.Conclusions: The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3602070]