Spinal Cord Segmentation - “Deep Learning based Medical image Segmentation
Overview
Medical imaging holds enormous potential, but turning raw scans into usable clinical insight is still far from simple.
Spinal MRI scans are complex, datasets are limited, and manual annotation is time-consuming and expensive. For teams working on spinal analysis, the challenge isn’t access to scans, it’s extracting accurate, reliable anatomical structures from them.
This project focused on building an end-to-end spinal cord segmentation system that converts MRI scans into precise, usable 3D representations of the spinal cord, vertebrae, and discs, while integrating cleanly into existing medical workflows.
Country
USA
Technology
Computer Vision · Deep Learning · Medical Image Segmentation · Cloud Deployment
Industry
Medical Imaging · Healthcare AI
The Challenge: Accurate Segmentation with Limited Data
Preparing MRI Data
The system received raw spinal MRI scans in DICOM format and required careful preprocessing before any deep learning model could process them accurately.
Meeting Clinical Accuracy
The model needed to deliver segmentation quality suitable for medical and research use, where even small inaccuracies could reduce trust and usability.
Limited Labeled Datasets
Public spinal datasets lacked reliable annotations, while manual segmentation of spinal cord, vertebrae, and discs required time, expertise, and consistent clinical accuracy.
Generalizing With Small Data
Small datasets increased overfitting risk and made it harder to build a model that performed consistently and improved as new annotated scans became available.
What Was Built: An End-to-End Spinal Segmentation Pipeline
Kreeda Labs built a full machine learning pipeline designed specifically for spinal MRI analysis.
MRI Ingestion & Preprocessing
The pipeline starts with raw spinal MRI scans:
- DICOM files ingested directly into the system
- Image normalization and preprocessing applied to improve consistency
- Preparation of scan volumes optimized for segmentation tasks
This step ensured clean, standardized input for downstream modeling.
Deep Learning–Based Segmentation Using MONAI
The core segmentation model was built using NVIDIA’s MONAI, a framework purpose-built for medical imaging.
- Applied deep learning and computer vision techniques for anatomical segmentation
- Leveraged transfer learning to improve performance on small datasets
- Tuned the model specifically for spinal MRI characteristics
The result was a highly accurate segmentation model tailored to spinal anatomy.
3D Reconstruction & Visualization
Segmentation outputs are converted into structured masks that enable:
- 3D rendering of the spinal cord, vertebrae, and discs
- Better visualization for analysis, simulation, and downstream applications
This bridges the gap between raw imaging data and actionable insight.
Dataset Creation & Mask Annotation Pipeline
One of the biggest challenges was the lack of labeled data.
To solve this, the team designed a custom annotation workflow:
- Defined clear annotation standards for spinal cord, vertebrae, and discs
- Created high-quality segmentation masks despite a small dataset size
- Focused on annotation precision over volume to maximize learning value
This approach made it possible to train a reliable model even with limited data.
Continuous Model Improvement
The pipeline was designed to evolve:
- New datasets can be added without retraining from scratch
- Model performance improves with every new batch of annotated data
- Supports long-term accuracy gains rather than one-off training
This makes the system future-ready and scalable.
Project Goals
Build a complete segmentation pipeline
From raw MRI scans to usable segmentation outputs and 3D models.
Handle data scarcity intelligently
Create a robust annotation and dataset-building process.
Integrate seamlessly with existing systems
No disruption to Multus Medical’s current workflows.
Achieve clinically meaningful accuracy
High Dice coefficient scores for spinal cord, vertebrae, and discs.
Support continuous improvement
Ensure the model improves as new data becomes available.
Technology Stack
Tech Stack | Tools |
|---|---|
AI / Deep Learning
| Medical image segmentation, transfer learning, deep neural networks |
Frameworks
| NVIDIA MONAI |
Image Processing | MRI preprocessing, mask generation, segmentation pipelines |
Backend & ML Pipeline
| Python-based ML services and processing workflows |
Deployment
| Cloud-based environment enabling remote execution |
Impact: High Accuracy, Real-World Usability
The system delivered strong, measurable results
90%+ segmentation accuracy, measured using a Dice coefficient of 0.9
Reduced dependency on large public datasets
Seamless integration into existing medical systems
Accurate segmentation of spinal cord, vertebrae, and discs
A reusable pipeline that improves with new data
Why This Matters
Medical imaging AI often fails not because of weak models—but because of poor data pipelines and unrealistic assumptions about dataset availability.
This project shows how careful system design, smart annotation strategies, and medical-first ML frameworks can deliver reliable results even in data-constrained environments.
It turns MRI scans into structured, actionable anatomical data—without overcomplicating the workflow.
Beyond Delivery: Built to Grow
The system is designed for long-term use, with
Ongoing dataset expansion
Future clinical and research applications
Continuous model refinement
Support for additional spinal structures