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Spine Segmentation
Key Investigators
- Ron Alkalay (Beth Isreal Deaconess, Boston)
- Steve Pieper (Isomics)
- Andres Diaz-Pinto (KCL)
- Juan Ruiz (Ebatinca, ULPGC)
- YOU
Project Description
Investigate and implement methods to segment the human spine from CT scans. See last Project Week’s page for background.
Objective
- Ideal segmentation will independently segment and label the vertebral bodies.
- We want the system to integrate with Slicer’s segmentation infrastructure.
- We think a deep learning approach using MONAILabel will be useful for this.
Approach and Plan
- Learn as much as possible about MONAILabel
- Investigate VerSe and if possible port it to Slicer/MONAI
- Figure out if/how we can use spine CTs from IDC for training.
Progress and Next Steps
- Held many productive discussions and worked on training with the VerSe public data
- Exchanged notes with the other MONAI Label projects
- Installing MONAI Label at BIDMC machines to train on cadeveric and patient spine scans
- Plan to make single-vertebra models for faster training of high resolution models (tractable on smaller GPU memory footprint)
Illustrations
Current effort
Initial effort
Background and References
- https://github.com/anjany/verse
- https://projectweek.na-mic.org/PW35_2021_Virtual/Projects/SpineSegmentation/