Preliminary study on statistical shape model applied to diagnosis of liver cirrhosis

Kohara, Shinya; Tateyama, Tomoko; Foruzan, Amir Hossein; Furukawa, Akira; Kanasaki, Shuzo; Wakamiya, Makoto; Xiong, Wei; Chen, Yen‐Wei

doi:10.1109/icip.2011.6116271

Cited by 15 publications

(13 citation statements)

References 12 publications

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“…Quantifying and dealing with such uncertainties has been explored in biomedical modeling and computational simulation to support diagnosis (González Ballester et al, 2000, 2002, 2004; Kohara et al, 2011; Schmid et al, 2011), pre and post-operative decisions (Talib et al, 2005; Rajamani et al, 2007; Bode et al, 2012), and therapy treatments (Belenguer et al, 2006; Zheng et al, 2007, 2009; Bednarz et al, 2011; Cao et al, 2012; Rüegsegger et al, 2012). Uncertainties influence in both the computation of the output and its interpretation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Mangado

Piella

Noailly

et al. 2016

Front. Bioeng. Biotechnol.

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Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

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Section: Introductionmentioning

confidence: 99%

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Mangado

Piella

Noailly

et al. 2016

Front. Bioeng. Biotechnol.

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“…The SSM has also been applied to automatic segmentation of medical images [8][9][10]. In our previous works, we constructed a statistic shape model (SSM) of the liver and shown that coefficients of the model can be used for classification of cirrhotic livers and normal livers [11,12]. We also proposed to use multiple statistical shape models (the liver SSM, the spleen SSM and a joint SSM of the liver and the spleen) and a correlation based effective mode selection method to improve the classification accuracy [13].…”

Section: Introductionmentioning

confidence: 99%

Quantifying stage progress of cirrhotic livers based on statistic shape models

Chen

Dong

Han

et al. 2013

2013 6th International Conference on Biomedical Engineering and Informatics

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It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we show a preliminary study on quantifying stage progress of cirrhotic liver based on statistical shape models (SSMs). We constructed not only the liver SSM, but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctor's opinions (stage labels). Both normal and abnormal livers including temporal sequence data are projected to the subspace with the selected modes. Most of the normal data are located in the center, while abnormal data are scattered around the normal data. If the disease progresses, data will move outside. The distance to the center can be used to quantify the stage progress of the cirrhotic liver.

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“…The SSM has also been applied to automatic segmentation of medical images [15–17]. In our previous works, we constructed a statistic shape model (SSM) of the liver and shown that coefficients of the model can be used for classification of cirrhotic livers and normal livers [18, 19]. The classification accuracy was about 60%–80%, which is depending on the number of training samples.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis and Quantification of Cirrhotic Livers Based on Morphological Analysis and Machine Learning

Chen

Luo

Dong

et al. 2013

Computational and Mathematical Methods in Medicine

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It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the liver SSM but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctor's opinions (stage labels). We then learn a mapping function between the selected mode and the stage of chronic liver. The mapping function was used for diagnosis and staging of chronic liver diseases.

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Preliminary study on statistical shape model applied to diagnosis of liver cirrhosis

Cited by 15 publications

References 12 publications

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Quantifying stage progress of cirrhotic livers based on statistic shape models

Computer-Aided Diagnosis and Quantification of Cirrhotic Livers Based on Morphological Analysis and Machine Learning

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