Attention-driven framework to segment renal ablation zone in posttreatment CT images: a step toward ablation margin evaluation

We used 3D CT images with RAZs and, for the first time, addressed deep-learning-based RAZ segmentation using parallel CT images. Our framework can effectively segment RAZs, allowing clinicians to automatically determine the ablation margin, making our tool ready for clinical use. Prediction time is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>∼</mml:mo> <mml:mn>1</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>s</mml:mi></mml:mrow> </mml:math> per patient, enabling clinicians to perform quick reviews, especially in time-constrained settings.