Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.00743

Synthetic-to-Real Transfer in Cerebral Microbleed Generation and Segmentation

To-Liang Hsu·Ting-Yu Lai·Ching-Ting Lin·Chun-Hao Huang·Wei-Chun Wang
View original source ↗Read source document ↗Export metadata ↓

Abstract

The development of automated cerebral microbleed (CMB) detection models is hindered by the low prevalence of CMBs and the high cost of expert annotation. To address this limitation, we developed a synthetic CMB generation pipeline and investigated the effectiveness of synthetic lesions for training deep learning detectors. Models trained solely on synthetic data achieved substantial detection performance, reaching approximately 87% of the lesion sensitivity of their real-trained counterparts. We further investigated the complementary roles of synthetic and real data under different training paradigms, revealing that a significant performance gap remains. Moreover, we found that successful synthetic-to-real transfer is strongly dependent on the downstream detection architecture, providing new insight into both the potential and limitations of synthetic data for CMB detection.

Explore related subjects

eess.IV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

To-Liang Hsu, Ting-Yu Lai, Ching-Ting Lin, Chun-Hao Huang, Wei-Chun Wang. 2026-09-30. Synthetic-to-Real Transfer in Cerebral Microbleed Generation and Segmentation. https://arxiv.org/abs/2610.00743

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Trajectory Stitching for Solving Inverse Problems with Flow-Based Models

Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial latent code (noise), such that the flow output solves the inverse problem. However, this requires backpropagating through the entire generative trajectory, incurring high memory costs and numerical instability. We propose MS-Flow, which represents the trajectory as a sequence of intermediate latent states rather than a single initial code. By enforcing the flow dynamics locally and coupling segments through trajectory-matching penalties, MS-Flow alternates between updating intermediate latent states and enforcing consistency with observed data. This reduces memory consumption while improving reconstruction quality. We demonstrate the effectiveness of MS-Flow over existing methods on image recovery and inverse problems, including inpainting, super-resolution, and computed tomography.

eess.IV↗
arXiv2026

Rapid quantitative chemical composition mapping using model-based MRI reconstruction with field inhomogeneity correction

Magnetic resonance spectroscopic imaging methods are particularly attractive for chemical engineering applications, including the monitoring of chemical reactions, where a rapid assessment of spatial variations in chemical composition is required. Conventional approaches, such as chemical shift imaging, introduce an additional spectral-encoding dimension, which substantially increases acquisition time. Consequently, fast spatially resolved spectroscopy remains an active research topic. This work uses a model-based reconstruction framework that embeds a priori spectral knowledge of the involved chemical components into the forward model to accelerate composition mapping. It allows for the reconstruction of molar ratio maps for individual chemical components without acquiring high-resolution spectra. Extending from previous studies, the proposed model accounts for inhomogeneities of the main field, which become more pronounced in systems with larger bores relevant for process engineering. Phantom experiments employing a 2D multi-gradient echo sequence demonstrate the ability to determine molar ratios for chemical components with single peaks as well as multiple peaks in their spectra. The bias and precision of the method remain around 0.01 mol/mol and 0.09 mol/mol, respectively, for a 20 s scan, indicating suitability for dynamic processes. Finally, acquisition time can be reduced further by applying sparse k-space sampling, potentially shortening the scan to 5 s with only minor degradation in quantitative performance.

eess.IV↗
arXiv2026

MorphoBranch: A Fine-Structure-Preserving Workbench for Morphometric Analysis of Branched Cellular Structures

Background and Objectives: Fluorescence-labeled cellular arbors provide readouts of neuronal and microglial morphology, but fine and weakly labeled processes are prone to fragmentation and false connections that bias skeleton-based measurements. We present MorphoBranch, a fine-structure-preserving, human-reviewable workbench for morphometry of branched cellular structures. Methods: MorphoBranch combines a deterministic Morphometry Engine with an LLM-assisted Refinement Engine. The Mor- phometry Engine implements an image-to-graph workflow integrating multiscale structural evidence extraction, hysteresis segmen- tation, evidence-constrained skeleton refinement, and graph-based morphometry. The Refinement Engine maps natural-language requests to registered actions for parameter adjustment, preview execution, metric reporting, and unsupported-request handling, while image processing and quantitative computation remain deterministic and reviewable. Results: MorphoBranch was evaluated on two public neuronal axon datasets, AxonMIP and AxonStack, and the in-house Cell- Morph dataset of microglial fluorescence images. It achieved the highest Skeleton F1 and clDice and the lowest length-estimation error among the evaluated methods on all three datasets, while also achieving the highest Dice and IoU on AxonMIP and Axon- Stack. Across 150 natural-language tasks, the Refinement Engine achieved a 94.0% end-to-end success rate. Conclusions: These results demonstrate that MorphoBranch provides a reproducible, human-reviewable workflow for mor- phometric analysis of branched cellular structures. It supports fine-structure-preserving quantification across neuronal axon and microglial fluorescence images while maintaining inspectable and reproducible analysis workflows.

eess.IV↗