Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
565-05-012 ISMRM Abstract

Deep Prompt Initialization and Fine Tuning of SAM2 for Automatic 2D Lung MR Image Segmentation

Primary: Body - Lung
Secondary: Analysis Methods - Foundation Models
565-05-012 · Segmentation for Body Applications · Wednesday, 13 May, 4:00 PM–4:55 PM · Digital Posters Row F
Keywords: Lung MRI Image segmentation Deep Learning Segmentation Segment anything model SAM2
Accepted
Muduo Xu1,2,3, Haoyang Pei1,2,3, Hersh Chandarana2,3, Yao Wang1, Li Feng 2,3
1NYU Tandon School of Engineering, New York, United States of America
2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
3Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, United States of America
Presenting Author: Li Feng

Synopsis

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References

1. Ravi N, Gabeur V, Hu YT, Hu R, Ryali C, Ma T, Khedr H, Rädle R, Rolland C, Gustafson L, Mintun E. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714. 2024 Aug 1. https://doi.org/10.48550/arXiv.2408.00714 [doi]
2. Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo WY, Dollár P. Segment anything. InProceedings of the IEEE/CVF international conference on computer vision 2023 (pp. 4015-4026). https://doi.org/10.48550/arXiv.2304.02643 [doi]
3. Hu EJ, Shen Y, Wallis P, Allen-Zhu Z, Li Y, Wang S, Wang L, Chen W. Lora: Low-rank adaptation of large language models. ICLR. 2022 Apr 25;1(2):3. https://doi.org/10.48550/arXiv.2106.09685 [doi]
4. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. InInternational Conference on Medical image computing and computer-assisted intervention 2015 Oct 5 (pp. 234-241). Cham: Springer international publishing. https://doi.org/10.48550/arXiv.1505.04597 [doi]
5. Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems. 2015;28. https://doi.org/10.48550/arXiv.1506.04214 [doi]
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http://echo.ismrm.org/p/ISMRM2026/565-05-012