Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans.
Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans.
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DOI：
10.1109/tmi.2016.2636188
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发表时间：
2017-03
影响因子：
10.6
通讯作者：
Bagci U
中科院分区：
文献类型：
--
作者：
Hussein S;Green A;Watane A;Reiter D;Chen X;Papadakis GZ;Wood B;Cypess A;Osman M;Bagci U
在本文中，我们研究了自动检测白色和棕色脂肪组织使用正电子发射断层扫描/计算机断层扫描（PET/CT）扫描，并开发方法，这些组织的量化在全身和身体区域的水平。我们提出了一个病人特定的自动肥胖分析系统，有两个模块。在第一个模块中，我们从CT扫描中检测白色脂肪组织（WAT）及其两种亚型：内脏脂肪组织（VAT）和皮下脂肪组织（SAT）。这个过程通常依赖于手动或半自动分割，导致效率低下的解决方案。我们的新框架通过提出一种无监督学习方法来解决这一挑战，该方法将腹部区域的VAT与SAT分开，用于中心性肥胖的临床量化。该步骤之后是通过稀疏3D条件随机场（CRF）的上下文驱动的标签融合算法，用于体积肥胖分析。在第二个模块中，我们使用PET扫描自动检测，分割和量化棕色脂肪组织（BAT），因为与WAT不同，BAT是代谢活跃的。在使用PET识别BAT区域之后，我们利用来自PET和CT的不对称互补信息执行共同分割过程。最后，我们提出了一个新的概率距离度量区分BAT从非BAT地区。这两个模块通过基于单次学习的自动身体区域检测单元集成。对151个PET/CT扫描进行的实验评价在中心性肥胖和棕色脂肪定量方面都达到了最先进的性能。
In this paper, we investigate the automatic detection of white and brown adipose tissues using Positron Emission Tomography/Computed Tomography (PET/CT) scans, and develop methods for the quantification of these tissues at the whole-body and body-region levels. We propose a patient-specific automatic adiposity analysis system with two modules. In the first module, we detect white adipose tissue (WAT) and its two sub-types from CT scans: Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT). This process relies conventionally on manual or semi-automated segmentation, leading to inefficient solutions. Our novel framework addresses this challenge by proposing an unsupervised learning method to separate VAT from SAT in the abdominal region for the clinical quantification of central obesity. This step is followed by a context driven label fusion algorithm through sparse 3D Conditional Random Fields (CRF) for volumetric adiposity analysis. In the second module, we automatically detect, segment, and quantify brown adipose tissue (BAT) using PET scans because unlike WAT, BAT is metabolically active. After identifying BAT regions using PET, we perform a co-segmentation procedure utilizing asymmetric complementary information from PET and CT. Finally, we present a new probabilistic distance metric for differentiating BAT from non-BAT regions. Both modules are integrated via an automatic body-region detection unit based on one-shot learning. Experimental evaluations conducted on 151 PET/CT scans achieve state-of-the-art performances in both central obesity as well as brown adiposity quantification.