spinalcordtoolbox/spinalcordtoolbox
Nov 2024 – Aug 2025
2 Months active
Languages Used
PythonrstJSON
Technical Skills
Command Line Interface DevelopmentDeep LearningDeep Learning IntegrationDocumentationFull Stack DevelopmentGPU Computing
Nathan Molinier
PROFILE
Nathan Molinier
Worked on the spinalcordtoolbox/spinalcordtoolbox repository, focusing on deep learning integration and workflow enhancements for medical imaging. Delivered a GPU-accelerated integration of the Total Spine Segmentation tool, enabling automated segmentation of vertebrae, intervertebral discs, spinal cord, and spinal canal within the existing pipeline. Updated dependencies and documentation to ensure maintainability and seamless deployment. Additionally, implemented a flexible label-mapping feature for vertebrae quality control reports, allowing user-defined JSON mappings for customized reporting across datasets. Leveraged Python, PyTorch, and JSON for command line interface development, data serialization, and testing, resulting in scalable, adaptable solutions for research and clinical workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
380
Activity Months2
Your Network
3 peopleShared Repositories
3
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly highlights: delivered a flexible label-mapping enhancement for vertebrae QC reports in spinalcordtoolbox/spinalcordtoolbox. Introduced user-defined JSON mappings to map voxel values to structure names, enabling customized QC reporting across datasets. Updated the QC workflow to read the new mappings and added a runtime flag to specify the custom file (--custom-labels-file). The work aligns QC reporting with diverse data sources and prepares for broader adoption.
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly highlights: delivered a flexible label-mapping enhancement for vertebrae QC reports in spinalcordtoolbox/spinalcordtoolbox. Introduced user-defined JSON mappings to map voxel values to structure names, enabling customized QC reporting across datasets. Updated the QC workflow to read the new mappings and added a runtime flag to specify the custom file (--custom-labels-file). The work aligns QC reporting with diverse data sources and prepares for broader adoption.
August 2025
November 2024
2 Commits • 1 Features
Nov 1, 2024Month: 2024-11 Key features delivered: - Integrated Total Spine Segmentation tool (totalspineseg) into spinalcordtoolbox (SCT) to enable segmentation of vertebrae, intervertebral discs, spinal cord, and spinal canal; updated dependencies and documentation; integrated into the deep segmentation inference pipeline. - Enabled GPU acceleration by wiring the torch device directly to totalspineseg, improving segmentation throughput and performance. Major bugs fixed: - No major bugs reported this period; the focus was on feature integration and pipeline enhancements. Overall impact and accomplishments: - Business value: automated, scalable vertebral segmentation within SCT enables faster research workflows and potential clinical adoption; GPU acceleration reduces runtime and enables larger datasets; documentation and dependency updates improve maintainability. - Technical achievements: seamless external-tool integration, GPU-accelerated inference, end-to-end docs/dependency updates, and alignment with SCT processing pipeline. Technologies/skills demonstrated: - Python, PyTorch, GPU-accelerated inference, software integration, dependency management, documentation, and end-to-end pipeline engineering. Top achievements: - Integrated Total Spine Segmentation (totalspineseg) into SCT, enabling segmentation of vertebrae, intervertebral discs, spinal cord, and spinal canal; updated docs, dependencies, and the deep segmentation inference pipeline. - Enabled GPU acceleration by wiring torch.device to totalspineseg, improving segmentation performance. - Commit references: 89b1ffcbc649cb0f90573cb37aa378f1a3cd3b08; 64f55fdf022659db5d5c779f01c605d91ca7d052.
November 2024
2 Commits • 1 Features
Nov 1, 2024Month: 2024-11 Key features delivered: - Integrated Total Spine Segmentation tool (totalspineseg) into spinalcordtoolbox (SCT) to enable segmentation of vertebrae, intervertebral discs, spinal cord, and spinal canal; updated dependencies and documentation; integrated into the deep segmentation inference pipeline. - Enabled GPU acceleration by wiring the torch device directly to totalspineseg, improving segmentation throughput and performance. Major bugs fixed: - No major bugs reported this period; the focus was on feature integration and pipeline enhancements. Overall impact and accomplishments: - Business value: automated, scalable vertebral segmentation within SCT enables faster research workflows and potential clinical adoption; GPU acceleration reduces runtime and enables larger datasets; documentation and dependency updates improve maintainability. - Technical achievements: seamless external-tool integration, GPU-accelerated inference, end-to-end docs/dependency updates, and alignment with SCT processing pipeline. Technologies/skills demonstrated: - Python, PyTorch, GPU-accelerated inference, software integration, dependency management, documentation, and end-to-end pipeline engineering. Top achievements: - Integrated Total Spine Segmentation (totalspineseg) into SCT, enabling segmentation of vertebrae, intervertebral discs, spinal cord, and spinal canal; updated docs, dependencies, and the deep segmentation inference pipeline. - Enabled GPU acceleration by wiring torch.device to totalspineseg, improving segmentation performance. - Commit references: 89b1ffcbc649cb0f90573cb37aa378f1a3cd3b08; 64f55fdf022659db5d5c779f01c605d91ca7d052.
Activity
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Quality Metrics
Correctness90.0%
Maintainability86.6%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONPythonrst
Technical Skills
Command Line Interface (CLI)Command Line Interface DevelopmentData Serialization (JSON)Deep LearningDeep Learning IntegrationDocumentationFull Stack DevelopmentGPU ComputingMedical ImagingSoftware DevelopmentTesting
Repositories Contributed To
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