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Abstract
Cerebral microbleed (CMB) segmentation is complicated by extreme class imbalance and data scarcity due to low prevalence. Given the positive correlation between the prevalence of CMBs and aging, integrating age-related biomarkers offers a promising strategy to improve segmentation performance. In this study, we propose AgeCMBNet, a framework that leverages estimated chronological age derived from anatomical T1-weighted magnetic resonance imaging. A pre-trained Meta-matching model was used to estimate each subject’s chronological age. Based on the median age of the training cohort, we implement a two-branch decoder architecture where younger and older adults are processed through different decoders while sharing a common image encoder. Experiments on an in-house dataset and the public MICCAI VALDO 2021 Challenge dataset demonstrate that our approach outperforms conventional segmentation models and existing state-of-the-art methods. Furthermore, our model shows robust generalizability on the ATLAS v2.0 stroke dataset, which has a weaker correlation between estimated age and lesion volume. Our code is available at https://github.com/junmokwon/AgeCMBNet
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026/paper/0011_paper.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to the Code Repository
https://github.com/junmokwon/AgeCMBNet
Link to the Dataset(s)
https://valdo.grand-challenge.org/
BibTex
@InProceedings{KwoJun_Estimated_MICCAI2026,
author = { Kwon, Junmo AND Kim, Jonghun AND Kim, Taehyeon AND Park, Hyunjin},
title = { { Estimated Age-Guided Cerebral Microbleed Segmentation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 16884},
month = {September},
page = {pending}
}
Reviews
Review #1
- Please describe the contribution of the paper
This paper introduces age as an additional guide, proposes using a pre-trained model to estimate age, and designs two decoder branches based on age, achieving strong performance on the cerebral microbleed segmentation task.
- 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 problem addressed in this paper is widespread: CMB data suffer from extreme class imbalance and data scarcity. 2.The use of age as a guide in this paper is supported by strong rationale. 3.The experimental results validate the effectiveness of the proposed method.
- 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.Part of Figure 3 is difficult to understand. Two distinct peaks are shown in (a); why should t be chosen on the right side? 2.The paper states: “Compared to the younger decoder, the older decoder is more sensitive to cerebral microbleed (CMB) lesions. “ How was this conclusion reached? Structurally, the two decoders are identical, and no unique loss function constrains the training of the encoders. 3.The overall design idea of the proposed method is novel, but the implementation is overly simplistic. Moreover, it deviates somewhat from the term “guided” in the title — in this paper, age merely serves as a conditional branching tool rather than directly guiding the segmentation process.
- 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 authors claimed to release the source code and/or dataset upon acceptance of the submission.
- 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?
The motivation and overall idea of the paper are novel. However, the method design is relatively simple. Moreover, the use of age information is not a “guidance” as suggested in the title, but rather a conditional criterion.
- 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
Review #2
- Please describe the contribution of the paper
The authors propose sAgeCMBNet, a framework that leverages estimated chronological age derived from anatomical T1-weighted magnetic resonance imaging.
- 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 authors utilized separate younger and older segmentation decoders based on the strong positive correlation between chronological age and the prevalence of CMB lesions.
- 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 authors did not report variance or confidence intervals, and the paper does not include statistical significance testing. 2.The experiments excluded the 1.5T scans which could have bias for generalization. 3.There is no sensitivity analysis to age estimation noise.
- 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 has provided an anonymized link to the source code, dataset, or any other dependencies.
- 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?
Method
- 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
It’s proposing a segmentation algorithm/framework for cerebral micro bleeds
- 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.
- Clinical Value
- Using Brain age estimation as a proxy for missing demographics
- Comprehensive evaluation
- 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.
- Binary age stratification is simplistic
- The characterizations of the in-house dataset is not discussed
- the performance of each age group should’ve been discussed
- 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 authors claimed to release the source code and/or dataset upon acceptance of the submission.
- 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
- please update or remove the placeholder for link to code “Our code is available at XXX”
- 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?
- Solid study structure
- Clinical Value is establish
- Comprehensive evaluation
- 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
N/A
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.
I concur with the unanimous positive reviews (scores: 4, 4, 5). The paper demonstrates sufficient novelty and technical soundness for MICCAI. I recommend an early accept; no rebuttal is required.