Enabling Real-Time Colonoscopic Polyp Segmentation on Commodity CPUs via Ultra-Lightweight Architecture
AI Analysis Complete: Breakthroughs in Colonoscopy
The research introduces 'UltraSeg,' a family of ultra-lightweight deep learning models designed for real-time colonoscopic polyp segmentation on commodity CPUs. Current high-precision models require GPUs, limiting their deployment in resource-constrained settings like primary hospitals or mobile endoscopy units. UltraSeg-108K (0.108 M parameters) is optimized for single-center data, achieving ≥90 FPS on a single CPU core with >94% Dice score of a 31 M-parameter U-Net using only 0.4% of its parameters. UltraSeg-130K (0.13 M parameters) extends this to multi-center, multi-modal images, enhancing generalization with minimal parameter increase. The models achieve this through optimized encoder-decoder widths, constrained dilated convolutions, and a lightweight cross-layer fusion module, demonstrating a 'bottom-up' architectural design for extreme compression scenarios. This work sets a new baseline for CPU-native medical image processing, providing an immediately deployable solution.
Executive Impact: Key Performance Indicators
This research delivers significant improvements across critical metrics, redefining the capabilities of AI in resource-constrained medical imaging.
0 FPS
Frames Per Second (FPS) on a Single CPU Core
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Million Parameters (Max UltraSeg-130K)
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% Dice Score of 31M UNet Achieved
Deep Analysis & Enterprise Applications
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< 0.3M
Parameters for Real-Time CPU Inference: UltraSeg's extreme-compression design.
| Model | Key Architectural Features | CPU Performance Focus |
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| UltraSeg (Our Approach) |
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| U-Net Variants (Small/Tiny) |
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| LB-UNet/EGE-UNet |
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| FastSCNN/MobileUNet |
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UltraSeg Architectural Design Flow
Start with proven dermoscopic 'micro-backbone' (LB-UNet)
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Reshape channel list for stronger deep-semantics (colonoscopy)
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Replace opaque boundary GT generation (Canny edge detector)
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Redesign Predict-Gated Fusion (learnable weights)
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Introduce Enhanced Dilated Block (EDB) for multi-scale context
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Integrate Attention-Guided Fusion (AGF) for cross-layer attention
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Add Simple Spatial Attention (SSA) for global context filtering
0.7926
Average Dice Score (UltraSeg-130K): Unprecedented accuracy for extreme-compression.
| Model | Params (M) | Avg Dice | Single-Core FPS |
|---|---|---|---|
| UltraSeg-130K | 0.130 | 0.7926 | 90.3 |
| UltraSeg-108K | 0.108 | 0.7838 | 92.1 |
| UNet-Base | 31.0 | 0.8388 | 1.6 |
| UNet-Medium | 1.519 | 0.7822 | 13.8 |
| UNet-Light | 0.861 | 0.7734 | 24.6 |
| UNet-Small | 0.389 | 0.7440 | 41.6 |
| UNet-Tiny | 0.102 | 0.6583 | 95.6 |
| LB-UNet | 0.038 | 0.7466 | 109.7 |
Real-Time CPU Deployment for Rural Hospitals
A major challenge in AI-assisted colonoscopy is deploying high-performance models in resource-constrained environments like primary or rural hospitals, which lack expensive GPU infrastructure. Traditional models, requiring GPUs, are impractical. The UltraSeg family was specifically developed to address this gap.
By achieving >90 FPS on a single CPU core with a parameter budget of <0.13M, UltraSeg enables real-time, accurate polyp segmentation directly on existing hardware. This dramatically lowers the barrier to entry for AI in diagnostics, making advanced colonoscopy screening accessible in previously underserved clinical settings. This translates to earlier detection and improved patient outcomes where high-end hardware is not an option.
0.8038
Dice on Combined Multi-Modal Dataset: Demonstrating robust generalization.
| Model | Dice (Mixed) | Dice (ETIS-Larib External) | Dice (BKAI-IGH External) |
|---|---|---|---|
| UltraSeg-130K | 0.8038 | 0.6821 | 0.8001 |
| UltraSeg-108K | 0.7884 | 0.6736 | 0.7873 |
| UNet-B | 0.8478 | 0.7094 | 0.8163 |
| UNet-M | 0.8131 | 0.6295 | 0.7764 |
| LB-UNet | 0.7685 | 0.6437 | 0.7700 |
Adapting to Multi-Center, Multi-Modal Data
Colonoscopy images vary significantly across manufacturers (Olympus, Pentax, Fujifilm) and imaging modalities (WLE, NBI, BLI, LCI). Models trained on single datasets often fail to generalize. The UltraSeg-130K variant was specifically engineered to handle this variability.
Through cross-layer attention-guided fusion and training on diverse multi-center, multi-modal datasets like PolypDB and PolypGen, UltraSeg-130K shows superior generalization. It maintains robust performance even with highly imbalanced sample sizes across centers and modalities, ensuring consistent diagnostic accuracy regardless of the endoscopic system or imaging protocol used in a diverse clinical environment.
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Your AI Implementation Roadmap
A clear, phased approach to integrating UltraSeg into your clinical workflows, designed for minimal disruption and maximum impact.
Phase 1: Initial Assessment & Data Integration
Evaluate existing colonoscopy data infrastructure, define data input/output pipelines, and integrate initial datasets for model training. Focus on establishing secure data handling protocols and setting up the CPU-native inference environment.
Phase 2: Model Customization & Optimization
Tailor UltraSeg-108K/130K models to specific institutional data, fine-tuning for local image characteristics and clinical requirements. Implement constrained dilated convolutions and cross-layer fusion for optimal performance and generalization.
Phase 3: Real-Time Deployment & Validation
Deploy the optimized UltraSeg model on commodity CPUs for real-time inference. Conduct rigorous clinical validation to ensure accuracy, speed, and reliability in live colonoscopy video analysis, gathering feedback for iterative improvements.
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