Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease.
Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease.
复制标题
从常染色体显性多囊肾病患者有界腹部 MR 图像中自动分割肝脏和肝囊肿。
DOI：
10.1088/0031-9155/61/22/7864
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发表时间：
2016-11-21
影响因子：
3.5
通讯作者：
Bae KT
中科院分区：
文献类型：
--
作者：
Kim Y;Bae SK;Cheng T;Tao C;Ge Y;Chapman AB;Torres VE;Yu AS;Mrug M;Bennett WM;Flessner MF;Landsittel DP;Bae KT
肝脏和肝囊肿体积测量是评估常染色体显性遗传性多囊肾病（ADPKD）和多囊肝病（PLD）疾病进展的重要定量成像生物标志物。到目前为止，没有研究提出了自动分割和体积计算的肝脏和肝囊肿在这些人群中。在本文中，我们提出了一个自动分割框架，肝脏和肝囊肿有界腹部MR图像ADPKD患者。为了模拟ADPKD肝脏的形状和变化，生成肝脏位置的空间先验概率图（SPPM）和肝实质组织强度和囊肿形态的组织先验概率图（TPPM）。在三维水平集框架内制定，TPPM成功地捕获了肝实质组织和囊肿，而SPPM全局约束到所需的边界的肝脏的初始表面。基于肾囊肿的空间先验知识和距离图，通过TPPM、阈值化和假阳性减少的组合操作来提取肝囊肿。通过对肝脏分割的交叉验证，放射学专家和所提出的方法之间的一致性对于形状一致性为84%，对于由类内相关系数（ICC）评估的体积测量为91%。对于肝囊肿分割，参考方法和拟定方法之间的一致性为：囊肿体积ICC=0.91，囊肿-肝脏体积%ICC=0.94。
Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD). To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three-dimensional level set framework, the TPPMs successfully captured liver parenchymal tissues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross-validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra-class correlation coefficient (ICC). For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC=0.91 for cyst volumes and ICC=0.94 for % cyst-to-liver volume.