Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation
Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation
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DOI：
10.1118/1.3284530
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发表时间：
2010-02-01
期刊：
影响因子：
3.8
通讯作者：
Summers, Ronald M.
中科院分区：
文献类型：
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作者：
Linguraru, Marius George;Sandberg, Jesse K.;Summers, Ronald M.
方法：开发了一种临床工具，从257个腹部增强CT图像中分割肝脏和脾脏。肝正常51例，脾正常44例，脾肿大128例，肝肿大59例，肝部分切除23例。20多张来自公共场所的对比增强CT扫描，主要是病理肝脏的人工分割，用于测试该方法。数据是在不同制造商和不同分辨率的各种扫描仪上获得的。使用10个非对比CT扫描（5个男性和5个女性）的人工分割数据创建了肝脏和脾脏的概率图谱。器官位置在物理空间中建模，并归一化为解剖标志，剑突的位置。肝脏和脾脏图谱的构建和利用使得从腹部CT数据中自动量化肝脏/脾脏的体积和高度（肝中肝脏高度和头尾脾脏高度）成为可能。通过测地线活动轮廓，将患者特定的对比度增强特征传递到自适应卷积，以及对形状和位置误差的校正，逐步改进量化。结果：正常和病理标本的肝脏和脾脏均得到了良好的分割。肝脏的Dice/Tanimoto体积重叠率为96.2%/92.7%，体积/高度误差为2.2%/2.8%，均方根误差（RMSE）为2.3 mm，平均表面距离（ASD）为1.2 mm。脾脏定量导致95.2%/91% Dice/Tanimoto重叠，3.3%/1.7%体积/高度误差，1.1 mm RMSE, 0.7 ASD。脾脏和肝脏与临床/人工测量高度的相关性（R-2）分别为0.97和0.93 （p < 0.0001）。观察者间和自动手动测量肝脏和脾脏的体积/高度误差无显著差异（p>0.2）。结论：该算法对于正常和肿大的脾脏和肝脏，以及存在肿瘤和肝部分切除术后较大形态学改变的情况下的分割具有鲁棒性。自动计算机辅助工具的肝脏和脾脏成像生物标志物有可能从临床数据的常规分析中帮助诊断腹部疾病并指导临床管理。
Methods: A clinical tool was developed to segment livers and spleen from 257 abdominal contrast-enhanced CT studies. There were 51 normal livers, 44 normal spleens, 128 splenomegaly, 59 hepatomegaly, and 23 partial hepatectomy cases. 20 more contrast-enhanced CT scans from a public site with manual segmentations of mainly pathological livers were used to test the method. Data were acquired on a variety of scanners from different manufacturers and at varying resolution. Probabilistic atlases of livers and spleens were created using manually segmented data from ten noncontrast CT scans (five male and five female). The organ locations were modeled in the physical space and normalized to the position of an anatomical landmark, the xiphoid. The construction and exploitation of liver and spleen atlases enabled the automated quantifications of liver/spleen volumes and heights (midhepatic liver height and cephalocaudal spleen height) from abdominal CT data. The quantification was improved incrementally by a geodesic active contour, patient specific contrast-enhancement characteristics passed to an adaptive convolution, and correction for shape and location errors.Results: The livers and spleens were robustly segmented from normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 96.2%/92.7%, the volume/height errors were 2.2%/2.8%, the root-mean-squared error (RMSE) was 2.3 mm, and the average surface distance (ASD) was 1.2 mm. The spleen quantification led to 95.2%/91% Dice/Tanimoto overlaps, 3.3%/1.7% volume/height errors, 1.1 mm RMSE, and 0.7 ASD. The correlations (R-2) with clinical/manual height measurements were 0.97 and 0.93 for the spleen and liver, respectively (p < 0.0001). No significant difference (p>0.2) was found comparing interobserver and automatic-manual volume/height errors for liver and spleen.Conclusions: The algorithm is robust to segmenting normal and enlarged spleens and livers, and in the presence of tumors and large morphological changes due to partial hepatectomy. Imaging biomarkers of the liver and spleen from automated computer-assisted tools have the potential to assist the diagnosis of abdominal disorders from routine analysis of clinical data and guide clinical management.