Prediction of Frozen Region Growth in Kidney Cryoablation Intervention Using a 3D Flow-Matching Model
This work addresses the need for precise intraoperative guidance in kidney cryoablation to improve tumor eradication and tissue preservation, representing an incremental advance over conventional methods.
This study tackled the problem of predicting frozen region growth during kidney cryoablation by developing a 3D flow-matching model that uses intraoperative CT imaging, achieving an IoU score of 0.61 and a Dice coefficient of 0.75.
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 represent complex anatomical structures accurately. 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 segmentation masks, effectively capturing spatial and morphological changes over time. Quantitative analysis highlights the model robustness, demonstrating strong agreement between predictions and ground-truth segmentations. The model achieves an Intersection over Union (IoU) score of 0.61 and a Dice coefficient of 0.75. 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.