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Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis

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Abstract

Purpose

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.

Methods

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).

Results

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.

Conclusions

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.

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References

  1. Ellis EL, Mann DA (2012) Clinical evidence of the regression of liver fibrosis. J Hepatol 56(5):1171–1180. https://doi.org/10.1016/j.jhep.2011.09.024

    Article  PubMed  Google Scholar 

  2. Tsochatzis EA, Bosch J, Burroughs AK (2014) Liver cirrhosis. Lancet 383(9930):1749–1761. https://doi.org/10.1016/S0140-6736(14)60121-5

    Article  PubMed  Google Scholar 

  3. Ichida F, Tsuji T, Omata M, Ichida T, Inoue K, Kamimura T, Yamada G, Hino K, Yokosuka O, Suzuki H (1996) New Inuyama classification; new criteria for histological assessment of chronic hepatitis. Int Hepatol Commun 6(2):112–119. https://doi.org/10.1016/S0928-4346(96)00325-8

    Article  Google Scholar 

  4. Liaw YF (2013) Reversal of cirrhosis: an achievable goal of hepatitis B antiviral therapy. J Hepatol 59(4):880–881. https://doi.org/10.1016/j.jhep.2013.05.007

    Article  PubMed  Google Scholar 

  5. Cadranel JF, Rufat P, Degos F (2000) Practices of liver biopsy in France: results of a prospective nationwide survey. For the Group of Epidemiology of the French Association for the Study of the Liver (AFEF). Hepatology 32(3):477–481. https://doi.org/10.1053/jhep.2000.16602

    Article  CAS  PubMed  Google Scholar 

  6. Rockey DC, Bissell DM (2006) Noninvasive measures of liver fibrosis. Hepatology 43(Suppl 1):S113–S120. https://doi.org/10.1002/hep.21046

    Article  CAS  PubMed  Google Scholar 

  7. Wang QB, Zhu H, Liu HL, Zhang B (2012) Performance of magnetic resonance elastography and diffusion-weighted imaging for the staging of hepatic fibrosis: a meta-analysis. Hepatology 56(1):239–247. https://doi.org/10.1002/hep.25610

    Article  PubMed  Google Scholar 

  8. Tang A, Cloutier G, Szeverenyi NM, Sirlin CB (2015) Ultrasound elastography and MR elastography for assessing liver fibrosis: part 2, diagnostic performance, confounders, and future directions. Am J Roentenol 205:33–40. https://doi.org/10.2214/AJR.15.14553

    Article  Google Scholar 

  9. House MJ, Bangma SJ, Thomas M, Gan EK, Ayonrinde OT, Adams LA, Olynyk JK, St Pierre TG (2015) Texture-based classification of liver fibrosis using MRI. J Magn Reson Imaging 41(2):322–328. https://doi.org/10.1002/jmri.24536

    Article  PubMed  Google Scholar 

  10. Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S (2018) Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase MR images. Radiology 287(1):146–155. https://doi.org/10.1148/radiol.2017171928

    Article  PubMed  Google Scholar 

  11. Choi KJ, Jang JK, Lee SS, Sung YS, Shim WH, Kim HS, Yun J, Choi JY, Lee Y, Kang BK, Kim JH, Kim SY, Yu ES (2018) Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver. Radiology. https://doi.org/10.1148/radiol.2018180763

    Article  PubMed  PubMed Central  Google Scholar 

  12. Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D, Court LE, Gillies RJ, Moros EG (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 44(3):1050–1062. https://doi.org/10.1002/mp.12123

    Article  CAS  PubMed  Google Scholar 

  13. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Cl Sánchez (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005

    Article  PubMed  Google Scholar 

  14. Kudo M, Zheng RQ, Kim SR, Okabe Y, Osaki Y, Iijima H, Itani T, Kasugai H, Kanematsu M, Ito K, Usuki N, Shimamatsu K, Kage M, Kojiro M (2008) Diagnostic accuracy of imaging for liver cirrhosis compared to histologically proven liver cirrhosis. Intervirol 51(Suppl 1):17–26. https://doi.org/10.1159/000122595

    Article  Google Scholar 

  15. Ozaki K, Matsui O, Kobayashi S, Sanada J, Koda W, Minami T, Kawai K, Gabata T (2010) Selective atrophy of the middle hepatic venous drainage area in hepatitis C-related cirrhotic liver: morphometric study by using multidetector CT. Radiology 257(3):705–714. https://doi.org/10.1148/radiol.10100468

    Article  PubMed  Google Scholar 

  16. Hunt FOM, Lubner MG, Ziemlewicz TJ, Muñoz Del Rio A, Pickhardt PJ (2016) The liver segmental volume ratio for noninvasive detection of cirrhosis: comparison with established linear and volumetric measures. J Comput Assist Tomogr 40(3):478–484. https://doi.org/10.1097/RCT.0000000000000389

    Article  PubMed Central  Google Scholar 

  17. Hori M, Okada T, Higashiura K, Sato Y, Chen YW, Kim T, Onishi H, Eguchi H, Nagano H, Umeshita K, Wakasa K, Tomiyama N (2015) Quantitative imaging: quantification of liver shape on CT using the statistical shape model to evaluate hepatic fibrosis. Acad Radiol 22(3):303–309. https://doi.org/10.1016/j.acra.2014.10.001

    Article  PubMed  Google Scholar 

  18. Chen YW, Luo J, Dong C, Han X, Tateyama T, Furukawa A, Kanasaki S (2013) Computer-aided diagnosis and quantification of cirrhotic livers based on morphological analysis and machine learning. Comput Math Methods Med 2013:264809. https://doi.org/10.1155/2013/264809

    Article  PubMed  PubMed Central  Google Scholar 

  19. Foruzan AH, Chen YW, Hori M, Sato Y, Tomiyama N (2014) Capturing large shape variations of liver using population-based statistical shape models. Int J Comput Assist Radiol Surg 9(6):967–977. https://doi.org/10.1007/s11548-014-1000-5

    Article  PubMed  Google Scholar 

  20. Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12:229–244. https://doi.org/10.1007/s12021-013-9204-3

    Article  PubMed  PubMed Central  Google Scholar 

  21. Maitra S, Yan Y (2008) Principle component analysis and partial least squares: two dimension reduction techniques for regression: applying multivariate statistical models, vol 79. Casualty Actuarial Society, Quebec City

    Google Scholar 

  22. Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc Ser B (Statistical Methodology) 73:273–282. https://doi.org/10.1111/j.1467-9868.2011.00771.x

    Article  Google Scholar 

  23. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Statistical Methodology) 67(2):301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x

    Article  Google Scholar 

  24. Geladi P, Kowalski B (1986) Partial least squares regression: a tutorial. Anal Chim Acta 185:1–17. https://doi.org/10.1016/0003-2670(86)80028-9

    Article  CAS  Google Scholar 

  25. Wold S, Ruhe A, Wold H, Dunn WJ III (1984) The collinearity problem in linear regression: the partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 5(3):735–743. https://doi.org/10.1137/0905052

    Article  Google Scholar 

  26. Lekadir K, Albi X, Pereañez M, Frangi AF (2015) Statistical shape modeling using partial least squares: application to the assessment of myocardial infarction. In: Proceedings: revised selected papers of the 6th international workshop on statistical atlases and computational models of the heart. Imaging and modelling challenges, vol 9534, pp 130–139. https://doi.org/10.1007/978-3-319-28712-6_14

    Chapter  Google Scholar 

  27. Lekadir K, Hoogendoorn C, Pereanez M, Albà X, Pashaei A, Frangi AF (2014) Statistical personalization of ventricular fiber orientation using shape predictors. IEEE Trans Med Imaging 33(4):882–889. https://doi.org/10.1109/TMI.2013.2297333

    Article  PubMed  Google Scholar 

  28. Martinez AM, Kak AK (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233. https://doi.org/10.1109/34.908974

    Article  Google Scholar 

  29. Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720. https://doi.org/10.1109/34.598228

    Article  Google Scholar 

  30. Çiçek Ö, Abdulkadir A, Lienkamp S, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Proceedings of MICCAI, Springer, LNCS 9901, pp 424‒432. https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  31. Okada T, Shimada R, Hori M, Nakamoto M, Chen YW, Nakamura H, Sato Y (2008) Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model. Acad Radiol 15(11):1390–1403. https://doi.org/10.1016/j.acra.2008.07.008

    Article  PubMed  Google Scholar 

  32. Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med Image Anal 26(1):1–18. https://doi.org/10.1016/j.media.2015.06.009

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ (1999) Non-rigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–721. https://doi.org/10.1109/42.796284

    Article  CAS  PubMed  Google Scholar 

  34. IRTK https://biomedia.doc.ic.ac.uk/software/irtk/. Last accessed 28 Sept 2018

  35. Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563. https://doi.org/10.1016/j.media.2009.05.004

    Article  PubMed  Google Scholar 

  36. de Jong S (1993) SIMPLS: an alternative approach to partial least squares regression. Chemomet Intell Lab Sys 18(3):251–263. https://doi.org/10.1016/0169-7439(93)85002-X

    Article  Google Scholar 

  37. Cawley G, Talbot N (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 11:2079–2107

    Google Scholar 

  38. Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, Sulkowski M, Torriani FJ, Dieterich DT, Thomas DL, Messinger D, Nelson M, APRICOT Clinical Investigators (2006) Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 43(6):1317–1325. https://doi.org/10.1002/hep.21178

    Article  CAS  PubMed  Google Scholar 

  39. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  40. Aspert N, Santa-Cruz D, Ebrahimi T (2002) MESH: measuring errors between surfaces using the Hausdorff distance. In: Proceedings: IEEE international conference on multimedia and expo, Switzerland, Lusanne, pp 705–708. https://doi.org/10.1109/ICME.2002.1035879

  41. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy FM, Sonka M, Buatti J, Aylward SR, Miller JV, Pieper S, Kikinis R (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341. https://doi.org/10.1016/j.mri.2012.05.001

    Article  PubMed  PubMed Central  Google Scholar 

  42. R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  43. Cohen J (1988) Statistical power analysis for the behavioral sciences. Routledge Academic, New York. https://doi.org/10.1016/C2013-0-10517-X

    Book  Google Scholar 

  44. Abdalla EK, Vauthey JN, Couinaud C (2002) The caudate lobe of the liver: implications of embryology and anatomy for surgery. Surg Oncol Clin N Am 11(4):835–848. https://doi.org/10.1016/S1055-3207(02)00035-2

    Article  PubMed  Google Scholar 

  45. Kasahara K, Saito A, Takakuwa T, Yamada S, Matsuzoe H, Hontani H, Shimizu A (2018) A spatiotemporal statistical shape model of the brain surface during human embryonic development. Adv Biomed Eng 7:146–155

    Article  Google Scholar 

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Acknowledgements

The authors are grateful for Fukuda Norio, Yuki Suzuki, Steven Lim, Yukio Oshiro and Toshiyuki Okada for their contributions to this study. This research was supported by Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) Number 26108004/26461789/19K20711 and 17K10403.

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Authors and Affiliations

  1. Division of Information Science, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, Nara, 630-0192, Japan

    Mazen Soufi, Yoshito Otake, Kazuya Moriguchi & Yoshinobu Sato

  2. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Masatoshi Hori, Takashi Ota & Noriyuki Tomiyama

  3. Department of Gastroenterology, Ikeda Municipal Hospital, 3-1-18, Jonan, Ikeda, Osaka, 563-8510, Japan

    Yasuharu Imai & Yoshiyuki Sawai

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  1. Mazen Soufi
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Correspondence to Yoshinobu Sato.

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Soufi, M., Otake, Y., Hori, M. et al. Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis. Int J CARS 14, 2083–2093 (2019). https://doi.org/10.1007/s11548-019-02084-z

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