Collaborative multi organ segmentation by integrating deformable and graphical models.

Collaborative multi organ segmentation by integrating deformable and graphical models.
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DOI:
10.1007/978-3-642-40763-5_20
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发表时间:
2013
期刊:
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子:
--
通讯作者:
Metaxas D
Metaxas D
中科院分区:
其他
文献类型:
--
作者:
Uzunbaş MG;Chen C;Zhang S;Poh KM;Li K;Metaxas D

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器官分割是一个具有挑战性的问题,并取得了重大进展。可变形模型(DM)和图模型(GM)是基于最优化的图像分割方法的两大类。人们正在努力将两种类型的模型整合到一个框架中。然而,以前的方法不是设计用于同时和准确地分割多个器官。在本文中,我们提出了一种混合的多器官分割方法,集成DM和GM在一个耦合的优化框架。具体来说,我们表明,基于区域的可变形模型可以与马尔可夫随机场(MRF)集成,使得多个模型的演化由最大后验(MAP)推理驱动。它将全局和局部变形约束纳入一个统一的框架,用于同时分割图像中的多个对象。我们在多器官分割的两个具有挑战性的问题上验证了所提出的方法,结果是有希望的。
Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation methods. Efforts have been made on integrating two types of models into one framework. However, previous methods are not designed for segmenting multiple organs simultaneously and accurately. In this paper, we propose a hybrid multi organ segmentation approach by integrating DM and GM in a coupled optimization framework. Specifically, we show that region-based deformable models can be integrated with Markov Random Fields (MRF), such that multiple models’ evolutions are driven by a maximum a posteriori (MAP) inference. It brings global and local deformation constraints into a unified framework for simultaneous segmentation of multiple objects in an image. We validate this proposed method on two challenging problems of multi organ segmentation, and the results are promising.
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