Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis
書誌事項
- 公開日
- 2019-11-08
- 資源種別
- journal article
- 権利情報
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- http://www.springer.com/tdm
- http://www.springer.com/tdm
- DOI
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- 10.1007/s11548-019-02084-z
- 公開者
- Springer Science and Business Media LLC
この論文をさがす
説明
Liver shape variations have been considered as feasible indicators of liver fibrosis. However, current statistical shape models (SSM) based on principal component analysis represent gross shape variations without considering the association with the fibrosis stage. Therefore, we aimed at the application of a statistical shape modelling approach using partial least squares regression (PLSR), which explicitly uses the stage as supervised information, for understanding the shape variations associated with the stage as well as predicting it in contrast-enhanced MR images.Contrast-enhanced MR images of 51 patients with fibrosis stages F0/1 (n = 18), F2 (n = 15), F3 (n = 7) and F4 (n = 11) were used. The livers were manually segmented from the images. An SSM was constructed using PLSR, by which shape variation modes (scores) that were explicitly associated with the reference pathological fibrosis stage were derived. The stage was predicted using a support vector machine (SVM) based on the PLSR scores. The performance was assessed using the area under receiver operating characteristic curve (AUC).In addition to commonly known shape variations, such as enlargement of left lobe and shrinkage of right lobe, our model represented detailed variations, such as enlargement of caudate lobe and the posterior part of right lobe, and shrinkage in the anterior part of right lobe. These variations qualitatively agreed with localized volumetric variations reported in clinical studies. The accuracy (AUC) at classifications F0/1 versus F2‒4 (significant fibrosis), F0‒2 versus F3‒4 and F0‒3 versus F4 (cirrhosis) were 0.90 ± 0.03, 0.80 ± 0.05 and 0.82 ± 0.05, respectively.The proposed approach offered an explicit representation of commonly known as well as detailed shape variations associated with liver fibrosis stage. Thus, the application of PLSR-based SSM is feasible for understanding the shape variations associated with the liver fibrosis stage and predicting it.
収録刊行物
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- International Journal of Computer Assisted Radiology and Surgery
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International Journal of Computer Assisted Radiology and Surgery 14 (12), 2083-2093, 2019-11-08
Springer Science and Business Media LLC
