Abstract
Cerebralmicrobleeds(CMBs)andcorticalsuperficialsiderosis (cSS) are imaging markers of cerebral small vessel disease, but their automated segmentation is limited by the scarcity of positive cases and voxel-level annotations. We propose a synthetic training framework for long-tail haemorrhagic lesion segmentation that requires no real lesion annotations for training and leverages radiological description of the lesions. Starting from anatomical brain parcellations, the framework applies spatial augmentation and voxel resampling, procedurally inserts cSS and CMB labels using clinical priors on lesion location and morphology, and synthesises images through randomised intensity assignment, blurring, and Rician noise simulation. Models were trained on dynamically generated image–label pairs and evaluated against manual delineationsin10cSScasesand13CMBcases.Theproposedconfigurations outperformed classical filter baselines. For cSS, the hypointensityconstrainedmodelachievedhigherAUPRCandAUROCthantheFrangi filter(AUPRC:0.284vs0.083;AUROC:0.907vs0.731).ForCMBs,explicit synthesis of blood vessels as lesion mimics improved performance over the classical baseline (AUPRC: 0.538 vs 0.004; AUROC: 0.999 vs 0.968).Theseresultssupportourproposalasafeasiblestrategyfordatascarce haemorrhagic lesion segmentation.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/SASHIMI_058.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=QXJHl3MFG7
BibTex
@InProceedings{CaoYua_Synthetic_MICCAISAT2026,
author = { Cao, Yuan AND Dash, Sumeet AND Zachariadis, Antonia AND Schreiber, Stefanie AND Neumann, Katja AND Bernal, Jose},
title = { { Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17258},
month = {pending},
page = {pending}
}