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Abstract
With the increasing interest in thermal ablation therapies, patient-specific predictive simulations have become crucial for improving both preoperative planning and intraoperative corrections. Planning strategies typically rely on the solution of an inverse problem, yet high-fidelity finite element simulations remain computationally prohibitive when embedded within an optimization loop under strict clinical time constraints. In this work, we introduce a real-time deep learning surrogate model for predicting liver microwave ablation outcomes, trained exclusively on synthetic data and personalized through 3D perfusion maps that encode patient anatomy and local perfusion rates. This surrogate model takes as input the antenna position, power and ablation duration, and outputs the predicted necrosis volume within a few milliseconds.
Once integrated into an optimization loop, it becomes possible to propose antenna trajectories and ablation settings that maximize tumor coverage while minimizing damage to healthy tissue. We demonstrate this on clinical data via a physics-based optimization framework that computes optimal ablation parameters in under 2 minutes, critical for preoperative planning or intraoperative re-planning in microwave ablation. The surrogate model was validated on in vivo pre-clinical data where it achieved an average Dice coefficient of 0.82 for necrosis mask prediction.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026/paper/4859_paper.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to the Code Repository
N/A
Link to the Dataset(s)
3D-IRCADb-01 dataset: https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01/
BibTex
@InProceedings{NahIli_ADeep_MICCAI2026,
author = { Nahmed, Ilias AND Dettori, Francesco AND Duprez, Michel AND Alvarez, Pablo AND Cotin, Stéphane},
title = { { A Deep Learning Surrogate Model for Microwave Thermal Ablation: From Preoperative Planning to Intraoperative Re-planning } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 16893},
month = {September},
page = {pending}
}
Reviews
Review #1
- Please describe the contribution of the paper
This paper proposes a deep learning-based surrogate model for fast and patient-specific prediction of thermal damage in microwave ablation (MWA). The method replaces computationally expensive FEM simulations with a 3D neural network that takes as input anatomical perfusion maps, antenna configuration, and treatment parameters, and directly predicts the resulting ablation zone.
The key contribution lies in enabling accurate, near real-time estimation of thermal damage, achieving orders-of-magnitude speedup over conventional simulation while maintaining high agreement with both synthetic and in vivo data. Furthermore, the proposed model is integrated into an optimization framework for treatment planning, demonstrating improved tumor coverage and reduced damage to healthy tissue.
Overall, the work presents a practical and efficient approach for data-driven MWA planning, bridging the gap between physics-based accuracy and clinical usability.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The proposed deep learning-based surrogate model successfully replaces expensive FEM simulations while maintaining strong predictive accuracy. The combination of a 3D U-Net backbone with conditioning (via FiLM) and attention mechanisms allows the model to capture both local and global dependencies. Notably, the method achieves orders-of-magnitude speedup (milliseconds vs. minutes), which is a significant technical and practical contribution enabling real-time applications.
The paper provides a thorough evaluation across synthetic data, patient-specific anatomies, and in vivo experiments, demonstrating robustness and generalization. Beyond prediction accuracy, the authors validate the model in a clinically meaningful downstream task—treatment planning optimization—showing clear improvements in tumor coverage and healthy tissue preservation. This end-to-end demonstration strengthens the practical impact of the work.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
1.While the proposed surrogate model effectively approximates FEM simulations, it does not explicitly account for temperature-dependent changes in tissue properties (e. g. , thermal conductivity, perfusion, and dielectric properties), which are known to significantly influence heat propagation and ablation outcomes in MWA. This simplification may limit the physical fidelity of the predictions, especially under high-temperature regimes where tissue properties can change nonlinearly. Incorporating or explicitly modeling such effects could further improve the reliability and clinical relevance of the approach.
2.The model relies primarily on perfusion maps to represent patient-specific variability, which implicitly captures vascular effects but may not fully account for other important tissue-specific properties. In particular, differences between tumor and healthy tissue in terms of thermal and dielectric properties are not explicitly modeled. This simplification raises concerns about whether perfusion alone is sufficient to capture the full range of factors influencing heat propagation and ablation outcomes.
3.The experiments consider treatment durations in the range of 180–500 seconds, which may correspond to regimes where the thermal field is approaching a quasi steady-state. As a result, the model may not sufficiently capture the transient heating dynamics that occur in earlier stages (e. g. , 0–180 seconds), where rapid temperature changes and nonlinear effects are more pronounced. This limited temporal coverage raises concerns about the model’s ability to generalize to shorter-duration treatments or to accurately support real-time intraoperative decision-making during the initial phase of ablation.
4.The paper lacks sufficient methodological and implementation details to enable reproducibility. Key aspects such as network architecture specifications, training procedures, data generation pipeline, and hyperparameter settings are not described in enough detail to allow faithful reimplementation. Furthermore, no code or pretrained models are made available. Providing more comprehensive technical details or releasing code would significantly improve transparency and facilitate adoption by the community.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not provide sufficient information for reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(4) Weak Accept — marginally above the acceptance threshold, but would not mind if rejected, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
This paper addresses an important and clinically relevant problem by proposing a fast surrogate model for microwave ablation planning, significantly reducing computation time compared to FEM-based approaches while maintaining strong predictive performance. The work is well-motivated and demonstrates promising results across synthetic, patient-specific, and in vivo settings. In particular, the ability to enable near real-time prediction and integrate with a treatment optimization framework is a notable strength with clear practical implications.
However, several limitations prevent a stronger recommendation. From a methodological perspective, the approach relies on relatively standard architectures and simplifies the underlying physics, for example by not explicitly modeling temperature-dependent tissue properties and by primarily relying on perfusion to represent tissue heterogeneity. In addition, the training and evaluation are heavily based on synthetic data, with limited validation on real-world cases, raising concerns about generalization. The restricted range of treatment durations may also limit the model’s ability to capture early transient dynamics. Finally, the paper lacks sufficient implementation details to ensure reproducibility.
Overall, while the work is not without weaknesses, it presents a solid and practically meaningful contribution that is likely to be of interest to the MICCAI community, and thus merits a weak accept.
- Reviewer confidence
Very confident (4)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Review #2
- Please describe the contribution of the paper
This manuscript presents a surrogate-assisted treatment planning framework for microwave ablation (MWA). The authors use Maxwell + Pennes + Arrhenius FEM simulations to train a 3D U-Net-based surrogate with FiLM and self-attention, conditioned on a 3D perfusion map, antenna location heatmap, power, and duration, to predict the necrotic zone. The surrogate is then embedded into an optimization pipeline for two clinically relevant tasks: preoperative planning and intraoperative replanning after antenna displacement without reinsertion. The study is evaluated on synthetic FEM data, FEM simulations on patient anatomies, and three in vivo porcine liver experiments. The reported speedup over FEM is substantial, and the proposed pipeline addresses a clinically meaningful problem.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
1.The clinical motivation is well defined. Rather than aiming broadly at faster simulation, the work targets two concrete bottlenecks in current MWA workflows: treatment planning before the procedure and rapid replanning after antenna misplacement. 2.The engineering pipeline is relatively complete, since the surrogate is not presented only as a forward predictor but is integrated into a usable planning and replanning framework. 3.The focus on perfusion and heat-sink effects is well justified by the supporting physical analysis, and this is an appropriate modeling direction. 4.It is noteworthy that the model is trained entirely on synthetic data yet still shows reasonable performance on patient anatomies and in vivo porcine experiments.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
1.Although perfusion and heat-sink effects are presented as central to the method, the experimental results are reported mostly as global averages. The paper lacks stratified analysis on the difficult cases where these factors matter most, such as tumors close to major vessels, regions with strong local cooling, larger tumors, or geometrically elongated tumors. This issue affects both the necrosis prediction and the planning results. The authors already have the ingredients for such an analysis, including vessel segmentation, radius estimation, perfusion maps, tumor volume, and tumor shape. Reporting performance by vascular proximity, perfusion intensity, tumor size, and shape complexity would make the claims much more convincing and would better define the boundary of applicability of the method. 2.The paper emphasizes patient-specific perfusion, but it does not directly test how robust the surrogate is to physiological distribution shift. The synthetic training distribution appears relatively constrained, whereas the supplementary material suggests substantial inter-subject variability in perfusion. As a result, the current results demonstrate anatomy generalization more clearly than physiological generalization. A dedicated robustness analysis across in-distribution, boundary, and mildly out-of-distribution perfusion conditions would be important, and this should be feasible using synthetic data alone. 3.The validation of the optimization stage is still incomplete. For the 112 clinical planning cases, the main comparison is against the manufacturer chart, but this does not establish whether surrogate-guided optimization is consistent with high-fidelity FEM optimization. Since the planning stage depends on the surrogate not only for forward prediction but also for ranking candidate solutions, the paper should include FEM back-checking on at least a representative subset of optimized cases. Comparing surrogate-selected solutions with FEM-evaluated solutions, or ideally with FEM-optimal solutions, would provide much stronger support for the optimization claims. 4.The replanning experiments are too restricted to support a broad claim of intraoperative robustness. The current setup considers only a 5 mm axial displacement and optimizes a limited subset of parameters. This demonstrates feasibility in one simplified scenario, but it is not sufficient to show robustness to the broader range of intraoperative deviations that may occur in practice. At minimum, the authors should include sensitivity experiments with different displacement magnitudes, non-axial translations, and small angular perturbations, even if these are first performed in synthetic or FEM-based settings. 5.Some of the paper’s stronger claims are not yet sufficiently supported by targeted evidence. In particular, the contribution of FiLM and self-attention remains somewhat unclear because the ablation results appear close in overall performance, and the practical value of patient-specific perfusion is not fully disentangled from the gain brought simply by using an optimizer stronger than the manufacturer chart. These issues could be addressed by more targeted comparisons: evaluating architectural ablations on difficult subsets, and comparing planning performance under homogeneous perfusion, image-derived vessel perfusion, and fully personalized perfusion within the same optimization framework.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not provide sufficient information for reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(4) Weak Accept — marginally above the acceptance threshold, but would not mind if rejected, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
I recommend “weak accept” for this work. It is noteworthy that the model, trained entirely on synthetic data, still demonstrates reasonable performance on patient anatomies and in vivo porcine experiments. The reported speedup over FEM is substantial, and the proposed pipeline effectively addresses a clinically relevant challenge.
- Reviewer confidence
Very confident (4)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Review #3
- Please describe the contribution of the paper
The authors have developed a deep learning surrogate model for real-time microwave thermal ablation outcome prediction, which can be used for both pre- and intra-operative planning. It combines a 3D U-Net architecture with FiLM conditioning and self-attention to encode 3D perfusion maps and ablation parameters.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The proposed model decreases significantly the simulation/prediction time of traditional numerical approaches, e.g., FEM, from minutes to milliseconds, which makes it a strong tool especially for intra-operative replanning where real-time performance is essential. Another strength of the paper is that the model is trained exclusively on simulated data. This effectively tackles the common problem of data insufficiency in the medical field, demonstrating a viable path forward when clinical data is limited.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
1.The specific target tissues are not mentioned in the Abstract or the main body until Section 2.3, where hepatic vascular trees are finally introduced. This key information should be presented much earlier to provide necessary context. 2.While the authors mention that the “The model is conditioned on both intervention- and patient specific parameters”, then, they mention that “once trained, our network can be deployed without per-case retraining” and “we promote generalization across patients and clinical scenarios without requiring per-case re training”. This indicates that, in fact, the patient-specific factor is not taken into account during inference. 3.The paper lacks a comparison to other existing studies or state-of-the-art methods. As a result, it is unclear if the achieved Dice score is clinically sufficient; in this context, leaving tumor tissue or removing excess healthy tissue would have severe adverse effects on the patient. The authors should address this. 4.While the validation effort is noted, it is insufficient as there is no clinical ground truth provided. 5.The authors mention interpretability as a feature of the work, but they do not provide any examples or visualizations of interpretable results to support this claim. 7.The statement “Each vessel segment was dilated to radii sampled from a normal distribution based on phsyiological literature” needs to be supported by a reference. 8.Typos and grammar errors: -The phrase “after antenna misplacement approximately 30 s” is unclear; it is uncertain if the authors mean “within” a 30-s window. -The text uses both “FEM” and “FE” interchangeably; a single abbreviation should be used. Also, “MW ablations” should become “MWAs” -Several typos were noted, such as “phsyiological” and the repetition in “network can be be deployed.”
- Please rate the clarity and organization of this paper
Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not mention open access to source code or data, but provides a clear and detailed description of the algorithm to ensure reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(5) Accept — should be accepted, independent of rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
Traditional numerical methods (FEM, CFD, etc.) often suffer from limited computational efficiency. This bottleneck, combined with the general lack of clinical validation data, makes the integration of computational modeling into the medical field a difficult task. The major factor for my recommendation is that this paper has effectively “eliminated” the computational time barrier by providing results comparable to FEM in a fraction of the time. Furthermore, the authors have achieved a meaningful level of validation despite data scarcity challenges. While there are minor points to be addressed regarding clarity and formatting, the core contribution, enabling near-instantaneous simulation, is significant enough to warrant acceptance.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Author Feedback
We thank the reviewers for their positive assessment and constructive comments. We appreciate that the clinical motivation, computational speed-up, and validation effort were recognized. Due to space limits, we grouped the comments below: Choice of perfusion (R1,R2): We focused on perfusion because the bioheat equation is particularly sensitive to this parameter, as shown in our previous work. The input is a voxelwise perfusion map encoding anatomy, vessels and lesions. Depending on tumor type, perfusion may be higher or lower than that of healthy liver tissue. Here, tumors were modeled as hypervascular, but the same synthetic framework can accommodate other tumor types by adapting the sampled ranges. Thermal and dielectric properties are relevant, but this work deliberately focused on perfusion as the main variable. Patient-specific modeling (R3): By “patient-specific”, we refer to the input conditioning: the network is trained once, while inference uses the patient anatomy and perfusion map. Thus, the model is general in its weights but patient-specific in its inputs. Ablation-duration and replanning-perturbation ranges (R1,R2): The 180–500 s range was chosen to match clinical practice. Manufacturer charts are also provided in this range. The framework itself is not restricted to this interval, since synthetic training can include shorter or longer durations. Regarding replanning, validation used a 5 mm axial perturbation to remain consistent with our previous FE-based study. The optimization itself used a wider axial translation range and a stochastic gradient-free method. Non-axial translations and angular perturbations are important for planning validation, but fall outside the scope of replanning, which is deliberately constrained to corrections along the existing insertion axis to maximize tumor coverage while avoiding reinsertion. Synthetic data analysis, architecture, and interpretability (R2,R3): We acknowledge that physiological distribution was not exhaustively investigated. However, the in vivo ablations were performed under different local perfusion conditions, providing partial verification of robustness. A deeper analysis would provide stronger evidence, but within space constraints, we prioritized this external validation. Likewise, a full network ablation study with more cases would better highlight the architecture. However, we could only fit the compact study in Table 1, where the proposed architecture improved Dice on in vivo experiments by 2 points on average compared to the baselines. Finally, “interpretability” referred to the planning framework vis-a-vis our concurrent submission. As mentioned in the conclusion, FE simulations can still be integrated near the end of the loop to verify that the proposed plan remains physically consistent. Planning validation (R2): In the present work, the consistency of the planning tool was partially assessed through the replanning validation: we reproduced the setup of our previous FE-based replanning study and obtained similar results with a faster approach. Comparison, Dice, and healthy tissue damage (R3): To our knowledge, there are no directly comparable NN-based MWA planning studies. Existing surrogate studies mainly concern RFA, where the modality and control variables differ, making direct comparison not quite fair. Numerical MWA planning studies report Dice scores in a similar range [4,7,8,9]. To assess healthy tissue damage, we reported both coverage and Dice: coverage prioritizes complete tumor treatment, while Dice penalizes excessive ablation outside the target. The average Dice improved from 0.62 to 0.80, suggesting reduced unnecessary damage. Minor revisions (R3): In the camera-ready submission, we moved the target tissue information earlier, added the requested reference for vessel-radius sampling, and corrected the reported typos and terminology inconsistencies.
Meta-Review
Meta-review #1
- Your recommendation
Provisional Accept
- Please justify your decision. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. In case of an invitation for rebuttal, clarify which points are important to address in the rebuttal.
Overall all reviewers found the method novel and interesting.
However, there were several points of clarify reviewers (especially #1 and #2) ask for that could be addressed to strengthen scores. These include the range of parameters considered during training, how the model was validated, and comparison methods.