Cella Medical Solutions • Medical Imaging AI
Enhancing Surgical Planning with AI-Driven Segmentation and Classification of Oncological MRI Scans
Cella Medical Solutions has developed an AI-driven pipeline for patient-specific 3D reconstruction from oncological MRI, significantly enhancing surgical planning. The system integrates automatic MRI sequence classification (ResNet-based, >90% accuracy) and anatomical segmentation (modular nnU-Net v2). This leads to improved Dice scores for critical structures (e.g., hepatic vasculature, pancreas, musculoskeletal) compared to prior methods. The entire process for a full MRI case takes approximately four minutes. This innovation translates voxel-wise labels into navigable 3D models, offering a robust, personalized, and sequence-aware approach to medical image analysis for enhanced clinical utility.
Executive Impact: At a Glance
This AI pipeline offers transformative benefits for healthcare providers and patients: significantly reducing manual processing time, improving diagnostic accuracy for complex oncological cases, and enabling more precise surgical planning. The rapid processing (approx. 4 mins/case) drastically increases workflow efficiency, while superior segmentation (e.g., 20-22% Dice score improvement for hepatic vasculature) minimizes surgical risks and enhances patient outcomes. By providing detailed 3D models, Cella empowers surgeons with unparalleled anatomical insight, leading to personalized and safer interventions.
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MRI Classification Accuracy
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Processing Time per MRI Case
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Dice Score Improvement (Vascular)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
MRI Sequence Classification
Anatomical Segmentation
Clinical Integration & Validation
96%
Overall T1/T2 Classification Accuracy
Enterprise Process Flow
MRI Volume Input
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ResNet18 Classification (T1/T2)
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Route to Specific Segmentation Network
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nnU-Net Segmentation
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3D Model Reconstruction
| Classification Performance Across Training Stages | Description |
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| Analysis | The classification model progressively improved with dataset expansion and epoch refinement. |
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0.7265
Macro-Average Dice for T1 Vascular Segmentation
| Vascular Segmentation: Sequence-Specific vs. General | Description |
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| Analysis | Sequence-specific networks significantly enhance segmentation accuracy for vascular structures. |
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Abdominal Organ Segmentation Breakthrough
Challenge: Accurately segmenting diverse abdominal organs with varying sizes and morphological complexities (e.g., liver, pancreas, bile duct, tumors) from heterogeneous MRI data.
Solution: Developed modular nnU-Net models trained on combined T1/T2 datasets, leveraging a label imbalance strategy to maximize dataset utilization with partially annotated cases.
Outcome: Achieved strong performance for large, well-defined organs (liver, spleen, kidneys > 0.85 Dice). The combined MR network achieved the highest macro Dice of 0.678 across all abdominal organs, demonstrating superior robustness and generalization across a wide range of anatomical structures.
| Musculoskeletal Segmentation: T2-weighted Dominance | Description |
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| Analysis | T2-weighted acquisitions prove superior for delineating osseous structures due to contrast characteristics. |
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7.2
Mean Qualitative Score (General Network)
Qualitative Validation by Expert Radiologists
Challenge: Assess the clinical applicability, anatomical fidelity, and interpretability of the AI-generated segmentations by experienced medical professionals in unseen clinical cases.
Solution: Eight independent MRI studies (4 T1w, 4 T2w) were segmented using TotalSegmentator (SOTA), General Network (combined T1/T2), and Specific Network (modality-specific). A blinded radiologist scored fidelity and interpretability (0-10 scale).
Outcome: Proposed networks significantly outperformed SOTA (General Network mean 7.2, Specific Network mean 6.9, SOTA mean 3.6). Specific Network showed highest median (7.8), indicating superior fidelity and clinical usefulness despite being trained on fewer cases. Segmented models successfully integrated into Cella's 3D Planner for interactive visualization.
Advanced ROI Calculator
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Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
Our proven methodology ensures a smooth and effective integration of AI into your existing infrastructure.
Phase 1: Discovery & Strategy
Initial consultation to understand your unique challenges, data landscape, and strategic objectives. Define clear KPIs and a tailored AI adoption strategy.
Phase 2: Data Preparation & Model Training
Securely integrate and preprocess your medical imaging data. Custom model training and fine-tuning based on your specific anatomical targets and sequences.
Phase 3: Integration & Validation
Seamless API integration into your existing PACS/surgical planning systems. Rigorous clinical validation and performance testing with your expert radiologists.
Phase 4: Deployment & Optimization
Full-scale deployment of the AI pipeline. Continuous monitoring, performance optimization, and ongoing support to ensure maximum clinical utility and ROI.
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