Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
667-04-008 ISMRM Abstract

Deep Learning-Based Background Removal in FRACTURE MRI

Primary: Musculoskeletal - Bone
Secondary: Acquisition & Reconstruction - AI methods
667-04-008 · Novel Visualization and Assessment of Osseous Tissues · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row H
Keywords: Deep Learning Segmentation Bone imaging FRACTURE
Accepted
Masami Yoneyama1, Suranjita Ganguly 2,3, Daichi Murayama4, TAKAYUKI SAKAI4,5, Yogesh k Mariappan3,6, Tejas Shah7
1Philips Japan, Tokyo, Japan
2Philips Innovation Campus, Bengaluru, India
3Philips Healthcare, Bengaluru, India
4Radiology, Eastern Chiba Medical Center, Chiba, Japan
5Tsukuba International University, Tsuchiura, Japan
6Philips Healthcare, Best, Netherlands
7Clinical Science, Philips India Ltd., Bengaluru, India
Presenting Author: Suranjita Ganguly

Synopsis

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References

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2. Gascho D, Zoelch N, Tappero C, et al. FRACTURE MRI: Optimized 3D multi-echo in-phase sequence for bone damage assessment in craniocerebral gunshot injuries. Diagnostic and Interventional Imaging. 2020;101(9):611-615. doi:10.1016/j.diii.2020.02.010 [doi]
3. Ryuna Kurosawa , Hajime Yokota , Takayuki Sada , et al. FRACTURE-Angiography: simultaneous acquisition of bone imaging and angiography. International Society of Magnetic Resonance in Medicine. 2024;
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7. Zhao Y, Huang Z, Che H, et al. Segmentation of Brain Tissues from MRI Images Using Multitask Fuzzy Clustering Algorithm. Journal of Healthcare Engineering. 2023;2023. doi:10.1155/2023/4387134 [doi]
8. Benny Y, Wolf L. OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020;12371 LNCS(i):514-530. doi:10.1007/978-3-030-58574-7_31 [doi]
9. Dombrowski M, Reynaud H, Baugh M, Kainz B. Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models. Proceedings of the IEEE International Conference on Computer Vision. Published online 2023:988-998. doi:10.1109/ICCV51070.2023.00097 [doi]
10. Tian D, Mansour H, Vetro A. Depth-weighted group-wise principal component analysis for video foreground/background separation. Proceedings - International Conference on Image Processing, ICIP. 2015;2015-December:3230-3234. doi:10.1109/ICIP.2015.7351400 [doi]

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http://echo.ismrm.org/p/ISMRM2026/667-04-008