CryoFlow: Prediction of Frozen Region Growth in Kidney Cryoablation Using a 3D Flow-Matching

This study presents a 3D flow-matching model designed to predict the progression of the frozen region (iceball) during kidney cryoablation. Precise intraoperative guidance is critical in cryoablation to ensure complete tumor eradication while preserving adjacent healthy tissue. However, conventional methods, typically based on physics-driven or diffusion-based simulations, are computationally demanding and often struggle to accurately represent complex anatomical structures. To address these limitations, our approach leverages intraoperative CT imaging to inform the model. The proposed 3D flow-matching model is trained to learn a continuous deformation field that maps early-stage CT scans to future predictions. This transformation not only estimates the volumetric expansion of the iceball but also generates corresponding masks, effectively capturing spatial and morphological changes over time. Quantitative analysis highlights the model’s robustness, showing strong agreement between predictions and ground-truth masks. The model achieves an Intersection over Union (IoU) score of 0.61 ± 0.11 and a Dice coefficient of 0.75 ± 0.11. By integrating real-time CT imaging with advanced deep learning techniques, this approach has the potential to enhance intraoperative guidance in kidney cryoablation, improving procedural outcomes and advancing the field of minimally invasive surgery. All codes are available https://github.com/siyeopyoon/CryoFlow.