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Automatic Segmentation of Kidneys from Non-Contrast CT Images Using Efficient Belief Propagation

文献类型： 会议论文

第一作者： Ronald M. Summers

作者： Ronald M. Summers 1 ; Jianfei Liu 1 ; Marius George Linguraru 2 ; Shijun Wang 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD USA 20892-1182

2.Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, USA 20010

关键词： Kidney segmentation;extracolonic finding;CT colonography;belief propagation;Markov random field

会议名称： Conference on computer-aided diagnosis

主办单位：

页码： 867005.1-867005.6

摘要： CT colonography (CTC) can increase the chance of detecting high-risk lesions not only within the colon but anywhere in the abdomen with a low cost. Extracolonic findings such as calculi and masses are frequently found in the kidneys on CTC. Accurate kidney segmentation is an important step to detect extracolonic findings in the kidneys. However, non-contrast CTC images make the task of kidney segmentation substantially challenging because the intensity values of kidney parenchyma are similar to those of adjacent structures. In this paper, we present a fully automatic kidney segmentation algorithm to support extracolonic diagnosis from CTC data. It is built upon three major contributions: 1) localize kidney search regions by exploiting the segmented liver and spleen as well as body symmetry; 2) construct a probabilistic shape prior handling the issue of kidney touching other organs; 3) employ efficient belief propagation on the shape prior to extract the kidneys. We evaluated the accuracy of our algorithm on five non-contrast CTC datasets with manual kidney segmentation as the ground-truth. The Dice volume overlaps were 88%/89%, the root-mean-squared errors were 3.4 mm/2.8 mm, and the average surface distances were 2.1 mm/1.9 mm for the left/right kidney respectively. We also validated the robustness on 27 additional CTC cases, and 23 datasets were successfully segmented. In four problematic cases, the segmentation of the left kidney failed due to problems with the spleen segmentation. The results demonstrated that the proposed algorithm could automatically and accurately segment kidneys from CTC images, given the prior correct segmentation of the liver and spleen.

分类号： r312

  • 相关文献

[1]Robust Detection of Renal Calculi from Non-contract CT Images Using TV-flow and MSER Features. Jianfei Liu,Shijun Wang,Marius George Linguraru,Ronald M. Summers. 2013

[2]Computer-aided Teniae Coli Detection Using Height Maps from Computed Tomographic Colonography Images. Zhuoshi Wei,Jianhua Yao,Shijun Wang,Ronald M. Summers. 2011

[3]Prediction of Polyp Histology on CT Colonography Using Content-Based Image Retrieval. Javed M. Aman,Jianhua Yao,Ronald M. Summers. 2010

[4]Automatic Colonic Polyp Shape Determination using Content-Based ImageRetrieval. Javed M. Aman,Jianhua Yao,Ronald M. Summers. 2011

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