Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features

Wolf‐Dieter Vogl1, Katja Pinker2, Thomas H. Helbich2, Hubert Bickel2, G Grabner3, Wolfgang Bogner3, Stephan Gruber3, Zsuzsanna Bagó-Horváth4, Peter Dubsky5, Georg Langs1
1Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
2Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
3MR Center of Excellence, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
4Department of Pathology, Medical University Vienna, 1090, Vienna, Austria
5Department of Surgery, Medical University Vienna, 1090, Vienna, Austria

Tóm tắt

Từ khóa


Tài liệu tham khảo

Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 127:2893–2917

Baum M (1976) The curability of breast cancer. BMJ 1:439–442

Pinker K, Bogner W, Baltzer P et al (2014) Improved differentiation of benign and malignant breast tumors with multiparametric 18fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res 20:3540–3549

Ayer T, Ayvaci MU, Liu ZX, Alagoz O, Burnside ES (2010) Computer-aided diagnostic models in breast cancer screening. Imaging Med 2:313–323

Woods BJ (2008) Computer-aided detection of malignant lesions in dynamic contrast enhanced MRI breast and prostate cancer datasets. Dissertation, Ohio State University Available via http://rave.ohiolink.edu/etdc/view?acc_num=osu1218155270

Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31:198–211

Vyborny CJ, Giger ML, Nishikawa RM (2000) Computer-aided detection and diagnosis of breast cancer. Radiol Clin North Am 38:725–740

Morris E, Comstock C, Lee C, Lehman C, Ikeda D, Newstead G (2013) ACR BI-RADS® magnetic resonance imaging. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System Reston. American College of Radiology, VA, USA

Stoutjesdijk MJ, Fütterer JJ, Boetes C, van Die LE, Jager G, Barentsz JO (2005) Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging. Investig Radiol 40:355–362

Breiman L (2001) Random forests. Mach Learn 45:5–32

Pinker K, Grabner G, Bogner W et al (2009) A combined high temporal and high spatial resolution 3 Tesla MR imaging protocol for the assessment of breast lesions: initial results. Invest Radiol 44:553–558

Bogner W, Pinker-Domenig K, Bickel H et al (2012) Readout-segmented echo-planar imaging improves the diagnostic performance of diffusion-weighted MR breast examinations at 3.0 T. Radiology 263:64–76

Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044

Somer EJ, Benatar NA, O'Doherty MJ, Smith MA, Marsden PK (2007) Use of the CT component of PET-CT to improve PET-MR registration: demonstration in soft-tissue sarcoma. Phys Med Biol 52:6991–7006

Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16:641–647

Chen W, Giger ML, Bick U, Newstead GM (2006) Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys 33:2878–2887

Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804

Chen W, Giger ML, Li H, Bick U, Newstead GM (2007) Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 58:562–571

Agner SC, Soman S, Libfeld E et al (2011) Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging 24:446–463

Woods BJ, Clymer BD, Kurc T et al (2007) Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data. J Magn Reson Imaging 25:495–501

Nie K, Chen JH, Yu HJ, Chu Y, Nalcioglu O, Su MY (2008) Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 15:1513–1525

Gilhuijs KG, Giger ML, Bick U (1998) Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med Phys 25:1647–1654

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

Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238

Menze BH, Kelm BM, Masuch R et al (2009) A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10:213

Pinker K, Bogner W, Baltzer P et al (2014) Improved diagnostic accuracy with multiparametric magnetic resonance imaging of the breast using dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, and 3-dimensional proton magnetic resonance spectroscopic imaging. Invest Radiol 49:421–430

Oliver A, Freixenet J, Marti J et al (2010) A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 14:87–110

Elter M, Horsch A (2009) CADx of mammographic masses and clustered microcalcifications: a review. Med Phys 36:2052–2068

Chen W, Giger ML, Bick U (2006) A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad Radiol 13:63–72

Wu Q, Salganicoff M, Krishnan A, Fussell DS, Markey MK (2006) Interactive lesion segmentation on dynamic contrast enhanced breast MRI using a Markov model. Proc SPIE 6144:61444M-1–61444M-8. https://doi.org/10.1117/12.654308

Zheng Y, Englander S, Baloch S et al (2009) STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis. Med Phys 36:3192–3204

Agner SC, Xu J, Madabhushi A (2013) Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging. Med Phys 40:032305

Twellmann T, Lichte O, Nattkemper TW (2005) An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE Trans Med Imaging 24:1256–1266

Vignati A, Giannini V, De Luca M et al (2011) Performance of a fully automatic lesion detection system for breast DCE-MRI. J Magn Reson Imaging 34:1341–1351

Yao J, Chen J, Chow C (2009) Breast tumor analysis in dynamic contrast-enhanced MRI using texture features and wavelet transform. IEEE J Sel Top Signal Process 3:94–100

Gubern-Mérida A, Martí R, Melendez J et al (2015) Automated localization of breast cancer in DCE-MRI. Med Image Anal 20:265–274

Han D, Bayouth J, Song Q et al (2011) Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method. In: Székely G, Hahn HK (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg, pp 245–256

Meinel LA, Stolpen AH, Berbaum KS, Fajardo LL, Reinhardt JM (2007) Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. J Magn Reson Imaging 25:89–95

Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50:92–98

Levman J, Leung T, Causer P, Plewes D, Martel AL (2008) Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines. IEEE Trans Med Imaging 27:688–696

Szabó BK, Wiberg MK, Boné B, Aspelin P (2004) Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast. Eur Radiol 14:1217–1225

McLaren CE, Chen WP, Nie K, Su MY (2009) Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. Acad Radiol 16:842–851

Chen W, Giger ML, Newstead GM et al (2010) Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers. Acad Radiol 17:822–829

Magometschnigg HF, Baltzer PA, Fueger B et al (2015) Diagnostic accuracy of 18F-FDG PET/CT compared with that of contrast-enhanced MRI of the breast at 3 T. Eur J Nucl Med Mol Imaging 42:1656–1665

Pinker-Domenig K, Bogner W, Gruber S et al (2012) High resolution MRI of the breast at 3 T: which BI-RADS® descriptors are most strongly associated with the diagnosis of breast cancer. Eur Radiol 22:322–330