Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
352-02-014 / 352-02-014 ISMRM Abstract

AI-based Fully Automated Whole-Body MRI Analysis for Bone Metastasis in Prostate Cancer

Primary: Diffusion - Diffusion Analysis & Visualization
Secondary: Analysis Methods - Segmentation and Detection
352-02-014 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2
352-02-014 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2
Keywords: Diffusion-Weighted Imaging Image synthesis Deep Learning Segmentation Whole-Body MRI Bone metastasis
Accepted
Akira Kudo 1,2, Katsuyuki M Nakanishi3, Yasuhiko Yamane4, TAKUYA YUZAWA1, Yoshiro Kitamura1, Yuki Suzuki2, noriyuki tomiyama5, Masatoshi Hori2
1Medical Systems Research & Development Center, Fujifilm Corporation, Minato, Japan
2Department of Artificial Intelligence in Diagnostic Radiology, The University of Osaka Graduate School of Medicine, Suita, Japan
3Himedic Clinic Nakanoshima, Medical Corporation Himedic Clinic WEST, Osaka, Japan
4Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, Osaka, Japan
5Department of Diagnostic and Interventional Radiology, The University of Osaka Graduate School of Medicine, Suita, Japan
Presenting Author: Akira Kudo

Synopsis

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References

1. Nakanishi, K., Tanaka, J., Nakaya, Y., et al. (2022). Whole-body MRI: Detecting bone metastases from prostate cancer. Japanese Journal of Radiology, 40, 229–244. https://doi.org/10.1007/s11604-021-01205-6 [doi]
2. Kumaraswamy, A. K., Venkategowda, P. B., Patil, C. M., et al. (2020). Automatic Bone Segmentation on Whole body Diffusion-Weighted MRI using Deep Learning. International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting.
3. Candito, A., et al. (2022). Automated tool to quantitatively assess bone disease on Whole-Body Diffusion Weighted Imaging for patients with Advanced Prostate Cancer. In Proceedings of the Medical Imaging with Deep Learning (pp. 2–4). Zurich, Switzerland, July 6–8.
4. Candito, A., Holbrey, R., Ribeiro, A., Dragan, A., et al. (2024). Deep learning assisted atlas-based delineation of the skeleton from WholeBody Diffusion Weighted MRI in patients with malignant bone disease. Biomedical Signal Processing and Control, Vol. 92, 106099, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2024.106099. [doi]
5. Chen, C., Dou, Q., Chen, H., Qin, J., & Heng, P. A. (2019). Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, AAAI Press. https://doi.org/10.1609/aaai.v33i01.3301865 [doi]
6. Zhu, J. -Y, Park, T., Isola, P., Efros, A. (2017). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, IEEE International Conference on Computer Vision (ICCV)(pp.2242-2251), Venice, Italy, doi: 10.1109/ICCV.2017.244. [doi]
7. Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
8. Otsu N. (1979). A threshold selection method from gray-level histograms. in IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan., doi: 10.1109/TSMC.1979.4310076 [doi]
9. Takasu, M., Kondo, S., Akiyama, Y., Takahashi, Y., Maeda, S., Baba, Y., Kawase, T., Ichinohe, T., & Awai, K. (2020). Assessment of early treatment response on MRI in multiple myeloma: Comparative study of whole-body diffusion-weighted and lumbar spinal MRI. PloS one, 15(2), e0229607. https://doi.org/10.1371/journal.pone.0229607 [doi]
10. Candito, A., Holbrey, R., Ribeiro, A., Messiou, C., Tunariu, N., Koh, D.-M., & Blackledge, M. D. (2024). Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets. Bioengineering, 11(2), 130. https://doi.org/10.3390/bioengineering11020130 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/352-02-014