Personalized segmentation is an advanced medical imaging technique designed to generate patient-specific segmentation models. By leveraging patient-specific characteristics and data-driven approaches, this method provides precise and tailored segmentation results, particularly valuable for individualized clinical decisions and interventions.
- Patient-specific Modeling – Tailored segmentation models adjusted to each individual's unique anatomical structures.
- Adaptive Learning – Utilizes advanced learning algorithms to continuously adapt and refine segmentation accuracy.
- High Accuracy and Robustness – Achieves precise segmentation results even in challenging medical scenarios.
The personalized segmentation framework integrates:
- Deep Learning Models – State-of-the-art convolutional neural networks and transformer-based architectures.
- Evidential Deep Learning (EDL) – Provides pixel-level uncertainty estimates to guide accurate segmentation and model refinement.
- Active Learning Strategy – Incorporates expert annotations on the most uncertain cases, continuously improving model performance and uncertainty estimation reliability.
- Tumor segmentation in oncology
- Organ delineation for radiation therapy
- Lesion detection and monitoring