Abstract
Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specific planning of antenna insertion trajectory, power, andtreatmentduration.Accuratenumericalsimulationcanprovidephysically reliable ablation predictions; however, its high computational cost limits its use in optimization-based planning, where repeated forward evaluations are required. To address this issue, we propose a digital twin-based automatic planning framework that combines a neural ablation prediction model with a genetic algorithm. The model was trained on multiphysics simulation data generated from patient-specific tumor and vessel structures, antenna configurations, and treatment conditions, and was used as a fast forward model during planning. The prediction model achieved a Dice score of 95.1%, enabling accurate deep learningbased optimization. In 13 unseen planning cases, the proposed method improved ablation efficiency by 54.3% and shortened the antenna insertion trajectory length by 3.3% compared with clinician-defined planning, while maintaining broadly comparable target coverage and organdamage levels. Most generated plans were also judged clinically applicable by MWA specialists. Furthermore, the framework enabled approximately 420-fold faster planning than numerical-simulation-based planning, demonstrating its potential as a fast digital twin for quantitative and personalized MWA treatment planning. The code is available at: https://github.com/SeonAengCho/MWA-Planning.git
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/DT4H_008.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=5sURBKlkZD
BibTex
@InProceedings{ChoSeo_Automatic_MICCAISAT2026,
author = { Cho, Seonaeng AND Seo, Minjee AND Seol, Minju AND Park, Juil AND Kwon, Joon Ho AND Yoon, Kyungho},
title = { { Automatic Patient-Specific Microwave Ablation Planning Accelerated by a Physics-Guided Deep Learning Model } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17275},
month = {pending},
page = {pending}
}