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Segmentation of right ventricle (RV) in a cine cardiac magnetic resonance (CMR) image is essential for the diagnosis and therapy of cardiac diseases. Traditional image segmentation methods fail to achieve high accuracy due to the complex structure of RV. Multi-atlas frame, which transforms the segmentation into registration and fusion, has become one of the main segmentation methods of RV in recent years. In this paper, we suggest a new multi-atlas frame for the automatical and accurate segmentation of RV. Firstly, an adaptive affinity propagation algorithm is used to obtain a series of atlases, in which the atlas set most similar to the target image based on hausdorff distance and normalized mutual information is selected. Then, the target image is registered onto the selected atlas by using multi-resolution strategy-based affine transform and Diffeomorphic demons algorithm to generate a deformation field, which is applied to the label image to obtain coarse segmentation results of RV. Finally, the Consensus Level, Labeler Accuracy and Truth Estimation (COLLATE) algorithm is used to fuse the coarse segmentation result to obtain the RV. The 30 cine CMR datasets are applied to the retrospective analysis. The comparison between RV value from the present algorithm and that from the manual segmentation shows that the average dice index and hausdorff distance are 0.84 and 11.46 mm, respectively, the correlation coefficients and deviation means of endo-diastolic volume, endo-systolic volume and ejection fraction are 0.94, 0.90, 0.86, and 2.5113, –3.4783, 0.0341, respectively. Compared with convolutional neural networks, the new multi-atlas frame has an endo-systolic volume close to the manual result. The results show that the suggested method improves the accuracy and robustness of segmentation of RV from the effective atlas selection and multi-resolution Diffeomorphic demons algorithm-based registration, and it promises to be applied to clinical diagnosis.
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Keywords:
- cardiac magnetic resonance imaging /
- right ventricle /
- multi-atlas segmentation /
- diffeomorphic demons
[1] World Health Organization, http://origin.who.int/mediacentrse/factsheets/fs317/en/ [2019−4−17]
[2] 胡盛寿, 高润霖, 刘力生, 朱曼璐, 王文, 王拥军, 吴兆苏, 李惠君, 顾东风, 杨跃进, 郑哲, 陈伟伟 2019 中国循环杂志 34 209
Google Scholar
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Google Scholar
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图 1 结合多图谱和Diffeomorphic demons算法的右心室分割流程图
Figure 1. Flow diagram of right ventricular segmentation combined with multi-atlas and Diffeomorphic demons algorithm.
图 2 图谱图像聚类流程图
Figure 2. Atlas image clustering flow chart
图 3 四个右心室配准的典型例子 (a) 目标图像; (b) 图谱图像; (b) 标记图像; (d) 配准结果
Figure 3. Four examples of typical right ventricular registration: (a) Target image; (b) atlas image; (c) label image; (d) registration result.
图 4 RV标记图像的融合过程
Figure 4. RV label image fusion process
图 5 ED从基底到顶端的RV分割结果
Figure 5. RV contour at ED from base to apex.
图 6 ES从基底到顶端的RV分割结果
Figure 6. RV contour at ES from base to apex.
图 7 Dice指标的箱形图
Figure 7. Box diagram of the Dice index.
图 8 本文算法与金标准结果分析 相关性分析 (a) 舒张末期容积; (b) 收缩末期容积; (c) 射血分数; Bland-Altman分析 (d) 舒张末期容积; (e) 收缩末期容积; (f) 射血分数
Figure 8. Analysis of algorithm and gold standard results. Correlation analysis (a) EDV; (b) ESV; (c) EF; Bland-Altman analysis (d) EDV; (e) ESV; (f) EF.
图 9 深度学习与金标准结果分析 相关性分析 (a) 舒张末期容积; (b) 收缩末期容积; (c) 射血分数; Bland-Altman分析 (d) 舒张末期容积; (e) 收缩末期容积; (f) 射血分数
Figure 9. Analysis of deep learning and gold standard results. Correlation analysis (a) EDV; (b) ESV; (c) EF; Bland-Altman analysis (d) EDV; (e) ESV; (f) EF.
表 1 ED, ES的平均Dice指数和豪斯多夫距离
Table 1. Average Dice index and Hausdorff distance of ED and ES.
Dice HD/mm ED 0.87(0.10) 10.76(4.5) ES 0.81(0.16) 12.16(6.54) 
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[1] World Health Organization, http://origin.who.int/mediacentrse/factsheets/fs317/en/ [2019−4−17]
[2] 胡盛寿, 高润霖, 刘力生, 朱曼璐, 王文, 王拥军, 吴兆苏, 李惠君, 顾东风, 杨跃进, 郑哲, 陈伟伟 2019 中国循环杂志 34 209
Google Scholar
Hu S S, Gao R L, Liu L S, Zhu M L, Wang W, Wang Y J, Wu Z S, Li H J, Gu D F, Yang Y J, Zheng Z, Chen W W 2019 Chin. Circ. J. 34 209
Google Scholar
[3] Haddad F, Hunt S A, Rosenthal D N, Murphy D J 2008 Circulation 117 1436
Google Scholar
[4] Kutty S, Shang Q, Joseph N, Kowallick J T, Schuster A, Steinmetz M, Danford D A, Beerbaum P, Sarikouch S 2017 Int. J. Cardiol. 248 136
Google Scholar
[5] Frey B J, Dueck D 2007 Science 315 972
Google Scholar
[6] 张耀楠, 陈传慎, 康雁 2014 东北大学学报(自然科学版) 35 795
Google Scholar
Zhang Y N, Chen C S, Kang Y 2014 J. Northeast. Univ. 35 795
Google Scholar
[7] 王开军, 张军英, 李丹, 张新娜, 郭涛 2007 自动化学报 12 1242
Wang K J, Zhang J Y, Li D, Zhang X N, Guo T 2007 Acta Automatica Sin. 12 1242
[8] Zuluaga M A, Cardoso M J, Modat M, Ourselin S 2013 International Conference, FIMH London, UK, June 20−22, 2013 p174
[9] Lorenzovaldes M, Sanchezortiz G I, Mohiaddin R H, Rueckert D 2002 MICCAI Tokyo, Japan September 25−28, 2002 p642
[10] Bai W, Shi W, Oregan D P, Tong T, Wang H, Jamilcopley S, Peters N S, Rueckert D 2013 IEEE Trans. Med. Imaging 32 1302
Google Scholar
[11] Ou Y, Sotiras A, Paragios N, Davatzikos C 2011 Med. Image Anal. 15 622
Google Scholar
[12] Shi W Z, Zhuang X H, Pizarro L, Bai W J, Wang H Y, Tung K P, Edwards P J, Rueckert D 2012 MICCAI Nice, France October 1−5, 2012 p659
[13] Thirion J 1998 Med. Image Anal. 2 243
Google Scholar
[14] 周先春, 汪美玲, 周林锋, 吴琴 2015 64 024205
Google Scholar
Zhou X C, Wang M L, Zhou L F, Wu Q 2015 Acta Phys. Sin. 64 024205
Google Scholar
[15] Vercauteren T, Pennec X, Perchant A, Ayache N 2009 NeuroImage 45 S61
Google Scholar
[16] Asman A J, Landman B A 2011 IEEE Trans. Med. Imaging 30 1779
Google Scholar
[17] 王丽嘉, 苏新宇, 李亚, 胡立伟, 聂生东 2018 波谱学杂志 35 407
Google Scholar
Wang L J, Su X Y, Li Y, Hu L W, Nie S D 2018 Chin. J. Magn. Reson. 35 407
Google Scholar
[18] Awate S P, Zhu P, Whitaker R T 2012 MICCAI Nice, France October 1−5, 2012 p 103
[19] Kyhl K, Ahtarovski K A, Iversen K, Thomsen C, Vejlstrup N, Engstrom T, Madsen P 2013 Am. J. Physiol Heart Circ. Physiol. 305 H1004
Google Scholar
[20] Lei X L, Liu H, Han Y C, Cheng W, Sun J Y, Luo Y, Yang D, Dong Y, Chung Y C, Chen Y H 2017 J. Magn. Reson. Imaging 45 1684
Google Scholar
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