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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

06.03.2019 | Original Article

A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts

verfasst von: Tatyana Ivanovska, Thomas G. Jentschke, Amro Daboul, Katrin Hegenscheid, Henry Völzke, Florentin Wörgötter

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 10/2019

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Abstract

Purpose

The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities.

Methods

We present a pipeline for breast density estimation, which consists of intensity inhomogeneity correction, breast volume segmentation, nipple extraction, and fibroglandular tissue segmentation. For the segmentation steps, a well-known deep learning architecture is employed.

Results

The average Dice coefficient for the breast parenchyma is \(92.5\%\pm 0.011\), which outperforms the classical state-of-the-art approach by a margin of \(9\%\).

Conclusion

The proposed solution is accurate and highly efficient and has potential to be applied for big epidemiological data with thousands of participants.

Vorheriger Artikel Some germinal roots of AI and their impact on Computer Assisted Radiology and Surgery (CARS)

Nächster Artikel Computer-aided diagnosis of gastrointestinal stromal tumors: a radiomics method on endoscopic ultrasound image

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Altman DG, Bland JM (1994) Diagnostic tests. 1: sensitivity and specificity. Br Med J: BMJ 308(6943):1552CrossRef

Bland JM, Altman D (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327(8476):307–310CrossRef

Dalmış MU, Litjens G, Holland K, Setio A, Mann R, Karssemeijer N, Gubern-Mérida A (2017) Using deep learning to segment breast and fibroglandular tissue in mri volumes. Med Phys 44(2):533–546CrossRef

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

Eberl MM, Fox CH, Edge SB, Carter CA, Mahoney MC (2006) Bi-rads classification for management of abnormal mammograms. J Am Board Fam Med 19(2):161–164CrossRef

Gonzalez RC, Woods RE (2002) Digital image processing. Prentice Hall, Upper Saddle River

Greenspan H, van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35(5):1153–1159CrossRef

Gubern-Merida A, Kallenberg M, Mann RM, Marti R, Karssemeijer N (2015) Breast segmentation and density estimation in breast MRI: a fully automatic framework. IEEE J Biomed Health Inform 19(1):349–357CrossRef

Hou Z (2006) A review on MR image intensity inhomogeneity correction. Int J Biomed Imaging 2006:1e11CrossRef

10.

Ivanovska T, Gloger LWO, Hahn HK, Völzke H, Hegenscheid K (2015) Sliding level set-based boundary: fully automated dense breast segmentation in native MR mammograms. In: Proceedings of MICCAI-BIA p 57

11.

Ivanovska T, Laqua R, Wang L, Liebscher V, Völzke H, Hegenscheid K (2014) A level set based framework for quantitative evaluation of breast tissue density from mri data. PLoS ONE 9(11):1–19CrossRef

12.

Ivanovska T, Laqua R, Wang L, Schenk A, Yoon JH, Hegenscheid K, Völzke H, Liebscher V (2016) An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of mr images. Comput Med Imaging Graph 48:9–20CrossRef

13.

Jentschke TG, Hegenscheid K, Völzke H, Wörgötter F, Ivanovska T (2018) Segmentierung von brustvolumina in magnetresonanztomographiedaten unter der verwendung von deep learning. In: Proceedings of workshop Bildverarbeitung in der Medizin (BVM)

14.

John U, Hensel E, Lüdemann J, Piek M, Sauer S, Adam C, Born G, Alte D, Greiser E, Haertel U, Hense HW, Haerting J, Willich S, Kessler C (2001) Study of health in pomerania (ship): a health examination survey in an east german region: objectives and design. Sozial-und Präventivmedizin 46(3):186–194CrossRef

15.

McCormack VA, dos Santos Silva I (2006) Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Prev Biomark 15(6):1159–1169CrossRef

16.

Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE, pp 565–571

17.

Razavi M, Wang L, Gubern-Mérida A, Ivanovska T, Laue H, Karssemeijer N, Hahn HK (2015) Towards accurate segmentation of fibroglandular tissue in breast MRI using fuzzy c-means and skin-folds removal. In: International conference on image analysis and processing. Springer, pp 528–536

18.

Rosset A, Spadola L, Ratib O (2004) Osirix: an open-source software for navigating in multidimensional dicom images. J Digit Imaging 17(3):205–216CrossRef

19.

Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRef

20.

Smith TB, Nayak KS (2010) Mri artifacts and correction strategies. Imaging Med 2(4):445–457CrossRef

21.

Tice JA, Cummings SR, Ziv E, Kerlikowske K (2005) Mammographic breast density and the gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat 94(2):115–122CrossRef

22.

Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4itk: improved n3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320CrossRef

23.

Vachon CM, Kuni CC, Anderson K, Anderson VE, Sellers TA (2000) Association of mammographically defined percent breast density with epidemiologic risk factors for breast cancer (united states). Cancer Causes Control 11(7):653–662CrossRef

24.

Vincent R (2006) Brainweb: simulated brain database. Available online at http://www.bic.mni.mcgill.ca/brainweb/

25.

Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in mri. IEEE Trans Med Imaging 26(3):405–421CrossRef

26.

Zhuo J, Gullapalli RP (2006) Mr artifacts, safety, and quality control. Radiographics 26(1):275–297CrossRef

Titel: A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts
verfasst von: Tatyana Ivanovska
Thomas G. Jentschke
Amro Daboul
Katrin Hegenscheid
Henry Völzke
Florentin Wörgötter
Publikationsdatum: 06.03.2019
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 10/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-01928-y

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Abstract

Purpose

Methods

Results

Conclusion

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