Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
565-05-004 ISMRM Abstract

nnU-Net-based Deep Learning for Automated Segmentation and Detection of Non-Mass Enhancement Lesions in Breast MRI

Primary: Body - Breast
Secondary: Acquisition & Reconstruction - AI methods
565-05-004 · Segmentation for Body Applications · Wednesday, 13 May, 4:00 PM–4:55 PM · Digital Posters Row F
Keywords: Deep learning Breast MRI NnU-Net Non-mass enhancement (NME) Image segmentation and detection
Accepted
Yijiang Huang 1, Wenjie Xu2, Neng Wang2, Sikai Wu1, Yongyu AN3, Cui Zhang1, Lei Lv4, Zhede Zhao4, Zhiwen Yang4, Yimin Huang4, Changyu Zhou3, Yunzhu Wu5, Guoqun Mao1
1Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
2Department of Radiology,, Tongde Hospital of Zhejiang Province, Hangzhou, China
3The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
4shukun Co.Ltd, China
5MR Research Collaboration Team, Shanghai, China
Presenting Author: Yijiang Huang

Synopsis

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References

1. Arian A, Teymouri Athar MM, Nouri S et al (2025) Role of breast MRI BI-RADS descriptors in discrimination of non-mass enhancement lesion: A systematic review & meta-analysis. European Journal of Radiology 185. https://doi.org/10.1016/j.ejrad.2025.111996 [doi]
2. Kuhl CK, Strobel K, Bieling H, Leutner C, Schild HH, Schrading S (2017) Supplemental Breast MR Imaging Screening of Women with Average Risk of Breast Cancer. Radiology 283:361-370. https://doi.org/10.1148/radiol.2016161444 [doi]
3. Houser M, Barreto D, Mehta A, Brem RF (2021) Current and Future Directions of Breast MRI. J Clin Med 10. https://doi.org/10.3390/jcm10235668 [doi]
4. Loap P, Monteil R, Kirova Y, Vu-Bezin J (2025) Automated contouring for breast cancer radiotherapy in the isocentric lateral decubitus position: a neural network-based solution for enhanced precision and efficiency. Strahlenther Onkol 201:601-605. https://doi.org/10.1007/s00066-024-02364-x [doi]
5. Illan IA, Ramirez J, Gorriz JM et al (2018) Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Contrast Media Mol Imaging 2018:5308517. https://doi.org/10.1155/2018/5308517 [doi]
6. Dalmis MU, Vreemann S, Kooi T, Mann RM, Karssemeijer N, Gubern-Merida A (2018) Fully automated detection of breast cancer in screening MRI using convolutional neural networks. J Med Imaging (Bellingham) 5:014502. https://doi.org/10.1117/1.JMI.5.1.014502 [doi]
7. Kubota K, Mori M, Fujioka T, Watanabe K, Ito Y (2023) Magnetic resonance imaging diagnosis of non-mass enhancement of the breast. Journal of Medical Ultrasonics 50:361-366. https://doi.org/10.1007/s10396-023-01290-2 [doi]
8. Zhou J, Liu H, Miao H et al (2025) Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience. Eur Radiol 35:140-150. https://doi.org/10.1007/s00330-024-10922-1 [doi]
9. Huo L, Hu X, Xiao Q, Gu Y, Chu X, Jiang L (2021) Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images. Magnetic Resonance Imaging 82:31-41. https://doi.org/10.1016/j.mri.2021.06.017 [doi]
10. 1. Arian A, Teymouri Athar MM, Nouri S et al (2025) Role of breast MRI BI-RADS descriptors in discrimination of non-mass enhancement lesion: A systematic review & meta-analysis. European Journal of Radiology 185. https://doi.org/10.1016/j.ejrad.2025.111996 2. Kuhl CK, Strobel K, Bieling H, Leutner C, Schild HH, Schrading S (2017) Supplemental Breast MR Imaging Screening of Women with Average Risk of Breast Cancer. Radiology 283:361-370. https://doi.org/10.1148/radiol.2016161444 3. Houser M, Barreto D, Mehta A, Brem RF (2021) Current and Future Directions of Breast MRI. J Clin Med 10. https://doi.org/10.3390/jcm10235668 4. Loap P, Monteil R, Kirova Y, Vu-Bezin J (2025) Automated contouring for breast cancer radiotherapy in the isocentric lateral decubitus position: a neural network-based solution for enhanced precision and efficiency. Strahlenther Onkol 201:601-605. https://doi.org/10.1007/s00066-024-02364-x 5. Illan IA, Ramirez J, Gorriz JM et al (2018) Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Contrast Media Mol Imaging 2018:5308517. https://doi.org/10.1155/2018/5308517 6. Dalmis MU, Vreemann S, Kooi T, Mann RM, Karssemeijer N, Gubern-Merida A (2018) Fully automated detection of breast cancer in screening MRI using convolutional neural networks. J Med Imaging (Bellingham) 5:014502. https://doi.org/10.1117/1.JMI.5.1.014502 7. Kubota K, Mori M, Fujioka T, Watanabe K, Ito Y (2023) Magnetic resonance imaging diagnosis of non-mass enhancement of the breast. Journal of Medical Ultrasonics 50:361-366. https://doi.org/10.1007/s10396-023-01290-2 8. Zhou J, Liu H, Miao H et al (2025) Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience. Eur Radiol 35:140-150. https://doi.org/10.1007/s00330-024-10922-1 9. Huo L, Hu X, Xiao Q, Gu Y, Chu X, Jiang L (2021) Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images. Magnetic Resonance Imaging 82:31-41. https://doi.org/10.1016/j.mri.2021.06.017 10. Choi Y, Bang J, Kim SY, Seo M, Jang J (2024) Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net. Eur Radiol 34:5389-5400. https://doi.org/10.1007/s00330-024-10585-y [doi]

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http://echo.ismrm.org/p/ISMRM2026/565-05-004