Prediction of Frozen Region Growth in Kidney Cryoablation Intervention Using a 3D Flow-Matching Model
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
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.
