130-06-006 / 130-06-006 ISMRT Abstract

Automated Quantitative 3D MRI of Breast Density Using U-Net Segmentation: Improving Accuracy and Standardization

130-06-006 · Winning Poster Presentations · Saturday, 9 May, 11:50 AM–2:00 PM · Meeting Room 2.60

Keywords: Breast Magnetic resonance imaging Mammography Fibroglandular tissue Density assessment

Accepted

Yasuo Takatsu ¹, Masahiro Shimizu², Tokiko Nakamura³, Hajime Sagawa⁴, Kazuki Takano⁵, Shohei Harada⁶, Takanobu Yamashiro⁷, Yuriko Nohara⁸, Soma Kumasaka⁹

¹Molecular Imaging, Fujita Health University, Toyoake, Japan

²Juntendo University Shizuoka Hospital, Shizuoka, Japan

³Ministry of Health, Labour and Welfare, Japan

⁴Kyoto University Hospital, Kyoto, Japan

⁵Department of Molecular Imaging, Fujita Health University, Toyoake, Japan

⁶Fujita Health University Hospital, Toyoake, Japan

⁷Minoh City Hospital, Minoo, Japan

⁸National Institute of Information and Communications Technology, Koganei, Japan

⁹Gunma University Graduate School of Medicine, Japan

Presenting Author: Yasuo Takatsu

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References

1. McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2006;15:1159-69.

2. Lankton S, Tannenbaum A. Localizing region-based active contours. IEEE Trans Image Process. 2008;17:2029–39. https://doi.org/10.1109/TIP.2008.2004611. [doi]

3. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Navab N, Hornegger J, Wells WM, Frangi AF, Eds., Lecture Notes in Computer Science, vol. 9351, Cham: Springer, 2015, pp. 234-241, doi:10.1007/978-3-319-24574-4_28. [doi]

4. American College of Radiology. Breast Imaging Reporting and Data System (BI-RADS®) Atlas. 4th edition. Reston, VA: American College of Radiology; 2003.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/130-06-006