352-02-009 / 352-02-009 ISMRM Abstract

Anatomically consistent 3D connectivity framework for medical images

Primary: Analysis Methods - Data Processing

Secondary: Body - Whole Body Imaging

352-02-009 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2

Keywords: Spine Deep Learning Segmentation MRI Workflow optimization

Accepted

Chitresh Bhushan¹, Ashish Saxena ², Dattesh Dayanand Shanbhag²

¹GE HealthCare Technology and Innovation Center, Niskayuna, United States of America

²GE HealthCare, Bangalore, India

Presenting Author: Ashish Saxena

Synopsis

Motivation:

Goals:

Approach:

Results:

Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

References

1. Warszawer, Yehuda et al, TotalSpineSeg: Robust Spine Segmentation with Landmark-Based Labeling in MR, 2025, ResearchGate preprint, https://www.researchgate.net/publication/389881289_TotalSpineSeg_Robust_Spine_Segmentation_with_Landmark-Based_Labeling_in_MRI

2. Warszawer Y, Molinier N, Valošek J, Shirbint E, Benveniste PL, Achiron A, Eshaghi A and Cohen-Adad J. Fully Automatic Vertebrae and Spinal Cord Segmentation Using a Hybrid Approach Combining nnU-Net and Iterative Algorithm. Proceedings of the 32th Annual Meeting of ISMRM. 2024

3. Lessmann, N., van Ginneken, B., de Jong, P.A., Išgum, I., 2019. Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med. Image Anal. 53, 142–155.

4. van der Graaf, J.W., van Hooff, M.L., Buckens, C.F.M., Rutten, M., van Susante, J.L.C., Kroeze, R.J., de Kleuver, M., van Ginneken, B., Lessmann, N., 2024. Lumbar spine segmentation in MR images: a dataset and a public benchmark. Sci. Data 11, 264. https://doi.org/10.1038/s41597-024-03090-w [doi]

5. You, X., Gu, Y., Liu, Y., Lu, S., Tang, X., Yang, J., 2022. EG-Trans3DUNet: A single-staged transformer-based model for accurate vertebrae segmentation from spinal ct images, in: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). Presented at the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), IEEE. https://doi.org/10.1109/isbi52829.2022.9761551 [doi]

6. Fang, L., Wang, X. and Wang, M., 2021. Superpixel/voxel medical image segmentation algorithm based on the regional interlinked value. Pattern Analysis and Applications, 24(4), pp.1685-1698.

7. Khandelwal, P., Collins, D.L. and Siddiqi, K., 2021. Spine and individual vertebrae segmentation in computed tomography images using geometric flows and shape priors. Frontiers in Computer Science, 3, p.592296.

8. De Leener, Benjamin, et al. "SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data." Neuroimage 145 (2017): 24-43.

9. Saxena, Ashish, Chitresh Bhushan, Saumya Ghose, Uday Patil, and Dattesh Shanbhag. "Integrated Multi-label 3D Deep Learning Multi-task Model for Intelligent MR Spine Scan Planning."

10. Muggeo, V. M. (2003). Estimating regression models with unknown break‐points. Statistics in medicine, 22(19), 3055-3071.

11. Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83-85.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/352-02-009