Enterprise AI Analysis
Cytoplasmic Strings Analysis in Human Embryo Time-Lapse Videos using Deep Learning Framework
This paper introduces a groundbreaking two-stage deep learning framework for the automated detection and localization of Cytoplasmic Strings (CS) in human embryo time-lapse videos. Addressing the challenges of extreme class imbalance and subtle visual features, the framework leverages a novel Uncertainty-aware Contractive Embedding (NUCE) loss function, resulting in significant improvements in F1-score across various transformer backbones. Furthermore, the RF-DETR architecture demonstrates state-of-the-art performance for precise CS localization, establishing a critical foundation for integrating CS as a robust biomarker in advanced embryo assessment.
Executive Impact at a Glance
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Max F1-Score (NUCE Loss)
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Frames in Curated Dataset
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CS Positive Instances
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RF-DETR mAP@25
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85.1%
F1-score Improvement with NUCE Loss
Enterprise Process Flow
Manual Annotation
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Expert Verification
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Train Auto-annotation Model
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Automated Prediction
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Iterative Refinement
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Final Verified Dataset
| Feature | Baseline Losses | NUCE Loss |
|---|---|---|
| Class Imbalance Handling | Limited | Robust (Uncertainty-aware weighting) |
| Feature Clustering | Interwoven/Scattered | Compact, Well-separated |
| F1-score Improvement (Avg.) | Varied/None | +1.6% to +5.0% |
| Robustness to Uncertainty | Low | High |
RF-DETR for CS Localization
RF-DETR architecture achieved state-of-the-art performance in localizing extremely thin, low-contrast Cytoplasmic Strings (CS) structures. Its multi-scale refinement and query-based attention mechanisms were critical for capturing these subtle anatomical features, significantly outperforming other detection models.
Key Highlight: Achieved 93.1 mAP@25, demonstrating superior ability to localize fine-grained structures. This performance is a significant advancement for automated embryo assessment.
Tags: Object Detection, Transformer Models, Medical Imaging
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