Statistical Shape Modeling Using Partial Least Squares: Application to the Assessment of Myocardial Infarction

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9534
• 期：1
• 页码：130-139
• 全文大小：1,479 KB
• 参考文献：1.Hoogendoorn, C., Duchateau, N., Sánchez-Quintana, D., Whitmarsh, T., Sukno, F., De Craene, M., Lekadir, K., Frangi, A.: A high-resolution atlas and statistical model of the human heart from multislice CT. IEEE Trans. Med. Imaging 32(1), 28–44 (2013)CrossRef
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• 作者单位：Karim Lekadir (19)
  Xènia Albà (19)
  Marco Pereañez (19)
  Alejandro F. Frangi (20)
  
  19. Center for Computational Imaging and Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain
  20. Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Sheffield, Sheffield, UK
  
• 丛书名：Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
• ISBN：978-3-319-28712-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Statistical shape modeling (SSM) is a widely popular framework in cardiac image analysis, especially for image segmentation and computer-aided diagnosis. However, the conventional PCA-based models produce new axes of variation which are statistically motivated but thus are not necessarily clinically meaningful. In this paper, we propose an alternative method for statistical decomposition of the shape variability based on partial least squares (PLS). With this method, the model construction is achieved such that it is constrained by the specific clinical question of interest (e.g., estimation of disease state). To achieve this, instead of deriving modes of variation in the directions of maximal variation as in PCA, PLS searches for new axes of variation that correlate most with some output clinical response variables such as diagnostic labels, leading to a decomposition that is anatomically and clinically more meaningful. The validation carried out with 200 cases from the Cardiac Atlas Project database as part of the MICCAI 2015 challenge on SSM, including healthy and infarcted left ventricles, shows the strength of the proposed PLS-based statistical shape model, with 98 % prediction accuracy.