Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis
Tóm tắt
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.
Tài liệu tham khảo
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
Tsochatzis EA, Bosch J, Burroughs AK (2014) Liver cirrhosis. Lancet 383(9930):1749–1761. https://doi.org/10.1016/S0140-6736(14)60121-5
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
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
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
Rockey DC, Bissell DM (2006) Noninvasive measures of liver fibrosis. Hepatology 43(Suppl 1):S113–S120. https://doi.org/10.1002/hep.21046
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
IRTK https://biomedia.doc.ic.ac.uk/software/irtk/. Last accessed 28 Sept 2018
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
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
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
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
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302
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
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
R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Cohen J (1988) Statistical power analysis for the behavioral sciences. Routledge Academic, New York. https://doi.org/10.1016/C2013-0-10517-X
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
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