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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

01.09.2015 | Original Article

Computer-aided analysis of prostate multiparametric MR images: an unsupervised fusion-based approach

verfasst von: N. Betrouni, N. Makni, S. Lakroum, S. Mordon, A. Villers, P. Puech

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 9/2015

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Abstract

Objective

The aim of this study is to provide an automatic framework for computer-aided analysis of multiparametric magnetic resonance (mp-MR) images of prostate.

Method

We introduce a novel method for the unsupervised analysis of the images. An evidential C-means classifier was adapted for use with a segmentation scheme to address multisource data and to manage conflicts and redundancy.

Results

Experiments were conducted using data from 15 patients. The evaluation protocol consisted in evaluating the method abilities to classify prostate tissues, showing the same behaviour on the mp-MR images, into homogeneous classes. As the actual diagnosis was available, thanks to the correlation with histopathological findings, the assessment focused on the ability to segment cancer foci. The method exhibited global sensitivity and specificity of 70 and 88 %, respectively.

Conclusion

The preliminary results obtained by these initial experiments showed that the method can be applied in clinical routine practice to help making decision especially for practitioners with limited experience in prostate MRI analysis.

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Tanimoto A, Nakashima J, Kohno H, Shinmoto H, Kuribayashi S (2007) Prostate cancer screening: the clinical value of diffusion-weighted imaging and dynamic MR imaging in combination with T2-weighted imaging. J Magn Reson Imaging 25(1):146–152CrossRefPubMed

Tofts PS, Berkowitz B, Schnall MD (1995) Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magn Reson Med 33(4):564–568CrossRefPubMed

Quantitative Imaging Biomarkers Alliance (2012) DCE MRI Quantification profile. http://rsna.org/QIBA_.aspx

Bloch I (1996) Some aspects of dempster-shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recogn Lett 17(8):905–920CrossRef

Capelle AS, Colot O, Fernandez-Maloigne C (2004) Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information. Inf Fusion 5(3):203–216CrossRef

Chen SYJ, Lin WC, Chen CT (1993) Evidential reasoning based on Dempster-Shafer theory and its application to medical image analysis. Neural Stoch Methods Image Signal Process II 2032:35–46CrossRef

Masson MH, Denoeux T (2011) ECM:An evidential version of the fuzzy c-means algorithm. Pattern Recogn 41:1384–1397CrossRef

Makni N, Iancu A, Colot O, Puech P, Mordon S, Betrouni N (2011) Zonal segmentation of prostate using multispectral magnetic resonance images. Med Phys 38(11):6093–6105CrossRefPubMed

Chan W, Wells R, Mulkern S, Haker J, Zhang K, Zou S, Maier, Tempany C (2003) Detection of prostate cancer by integration of line-scan diffusion, t2-mapping and t2-weighted magnetic resonance imaging: a multichannel statistical classifier. Med Phys 30(9):2390–2398CrossRefPubMed

10.

Madabhushi A, Feldman MD, Metaxas DN, Tomaszeweski J, Chute D (2005) Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI. IEEE Trans Med Imaging 24(12):1611–1625

11.

Vos PC, Hambrock T, Hulsbergen-van de Kaa CA, Fütterer JJ, Barentsz JO, Huisman HJ (2008) Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI. Med Phys 35(3):888–899CrossRefPubMed

12.

Puech P, Betrouni N, Makni N, Dewalle AS, Villers A, Lemaitre L (2009) Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results. Int J Comput Assist Radiol Surg 4:1–10CrossRefPubMed

13.

Vos PC, Hambrock T, Barenstz JO, Huisman HJ (2010) Computer-assisted analysis of peripheral zone prostate lesions using t2-weighted and dynamic contrast enhanced t1-weighted MRI. Phys Med Biol 55(6):1719CrossRefPubMed

14.

Lopes R, Ayache A, Makni N, Puech P, Villers A, Mordon S, Betrouni N (2011) Prostate cancer characterization on MR images using fractal features. Med Phys 38(1):83–95CrossRefPubMed

15.

Niaf E, Rouvière O, Mège-Lechevallier F, Bratan F, Lartizien C (2012) Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI. Phys Med Biol 57(12):3833–3851CrossRefPubMed

16.

Shah V, Turkbey B, Mani H, Pang Y, Pohida T, Merino M, Pinto PA, Choyke PL, Bernardo M (2012) Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging. Med Phys 39(7):4093–4103PubMedCentralCrossRefPubMed

17.

Chesnais AL, Niaf E, Bratan F, Mège-Lechevallier F, Roche S, Rabilloud M, Colombel M, Rouvière O (2013) Differentiation of transitional zone prostate cancer from benign hyperplasia nodules: evaluation of discriminant criteria at multiparametric MRI. Clin Radiol 68(6):323–330CrossRef

18.

Hoeks CMA, Hambrock T, Yakar D, Hulsbergen-van de kaa CA, Feuth T, Witjes JA, Fütterer JJ, Barentsz JO (2013) Transition zone prostate cancer: detection and localization with 3-T multiparametric MR imaging. Radiology 266:207–217CrossRefPubMed

19.

Hambrock T, Vos PC, Hulbergen-van de Kaa CA, Barentsz JO, Huisman HJ (2013) Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging-effect on observer performance. Radiology 266(521):530

20.

Tiwari P, Rosen M, Madabhushi A (2009) A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). Med Phys 39(9):3927–3939

21.

Liu X, Langer DL, Haider MA, Yang Y, Wernick MN, Yetik (2009) Prostate cancer segmentation with simultaneous estimation of markov random field parameters and class. IEEE Trans Med Imaging 28(6):906–915CrossRefPubMed

22.

Ozer S, Langer D, Liu X, Haider M, van der Kwast T, Evans A, Yang Y, Wernick M, Yetik I (2010) Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. Med Phys 37(4):1873–1883CrossRefPubMed

23.

Lopes R, Ayache A, Makni N, Puech P, Villers A, Mordon S, Betrouni N (2011) Prostate Cancer characterization on MR images using fractal features. Med Phys 38(1):83–95CrossRefPubMed

24.

Lopes R, Betrouni N (2009) Fractal and multifractal analysis: a review medical image analysis. Med Image Anal 13(4):634–649CrossRefPubMed

25.

Postaire JG, Vasseur C (1981) An approximate solution to normal mixture identification with application to unsupervised pattern classification. IEEE Trans Pattern Anal Mach Intell 3(2):163–179CrossRefPubMed

26.

Denoeux T, Masson MH (2004) Evidential clustering of proximity data. IEEE Trans Syst Man Cybern 34:95–109CrossRef

27.

Smets P (2007) Analyzing the combination of conflicting belief functions. Inf Fusion 8(4):387–412CrossRef

28.

McNeal JE, Haillot O (2001) Patterns of spread of adenocarcinoma in the prostate as related to cancer volume. Prostate 49:48–57CrossRefPubMed

29.

Puech P, Potiron E, Lemaitre L, Leroy X, Haber GP, Crouzet S, Kamoi K, Villers A (2009) Dynamic contrast-enhanced–magnetic resonance imaging evaluation of intraprostatic prostate cancer: correlation with radical prostatectomy specimens. Urology 74(5):1094–1099CrossRefPubMed

30.

Tiwari P, Viswanath S, Kurhanewicz J, Madabhushi A (2011) Weighted combination of multi-parametric MR imaging markers for evaluating radiation therapy related changes in the prostate. In: Prostate cancer imaging. Image analysis and image-guided interventions. Lecture notes in computer science, vol 6963. Springer, Berlin, Heidelberg, pp 80–91

Titel: Computer-aided analysis of prostate multiparametric MR images: an unsupervised fusion-based approach
verfasst von: N. Betrouni
N. Makni
S. Lakroum
S. Mordon
A. Villers
P. Puech
Publikationsdatum: 01.09.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 9/2015
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-015-1151-z

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Abstract

Objective

Method

Results

Conclusion

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