arXiv subjects

Francesca Galassi

Publications and source records attributed to Francesca Galassi.

4 recordsLinked to original sources

arXiv2026

StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows

Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research environments due to, among other reasons, software dependencies and computational requirements. We introduce StrokeSeg2, a lightweight, modular, cross-platform C++/Qt framework designed to adapt resource-intensive 3D stroke segmentation pipelines into portable and reproducible applications. To improve compatibility with standard clinical workstations, we investigate the combined effect of architectural compression through knowledge distillation and inference optimisation using ONNX Runtime with Float16 quantisation. Across heterogeneous hardware configurations (CPU, integrated GPU, and dedicated GPU) architectural distillation emerged as the primary contributor to efficiency gains, contributing to over 90% reduction in energy consumption and an average 84% reduction in inference time. Specifically, we identify a 0.84M-parameter student model as the most favourable trade-off, reducing the original 102.3M-parameter teacher architecture to a 2.1 MB disk footprint while preserving robust lesion localisation and competitive segmentation performance. This small footprint supports the development of a self-contained installer for clinical workstation targets. Finally, StrokeSeg2 packages these optimisations into standalone installers for Windows, macOS, and Linux. By providing both graphical and commandline interfaces without Docker or external environment dependencies, StrokeSeg2 facilitates deployment of high-performance segmentation workflows for routine clinical research pipelines.

cs.CV↗

arXiv2025

Stroke Lesion Segmentation in Clinical Workflows: A Modular, Lightweight, and Deployment-Ready Tool

Deep learning frameworks such as nnU-Net achieve state-of-the-art performance in brain lesion segmentation but remain difficult to deploy clinically due to heavy dependencies and monolithic design. We introduce \textit{StrokeSeg}, a modular and lightweight framework that translates research-grade stroke lesion segmentation models into deployable applications. Preprocessing, inference, and postprocessing are decoupled: preprocessing relies on the Anima toolbox with BIDS-compliant outputs, and inference uses ONNX Runtime with \texttt{Float16} quantisation, reducing model size by about 50\%. \textit{StrokeSeg} provides both graphical and command-line interfaces and is distributed as Python scripts and as a standalone Windows executable. On a held-out set of 300 sub-acute and chronic stroke subjects, segmentation performance was equivalent to the original PyTorch pipeline (Dice difference $<10^{-3}$), demonstrating that high-performing research pipelines can be transformed into portable, clinically usable tools.

cs.CV↗

arXiv2025

Unsupervised Detection of Post-Stroke Brain Abnormalities

Post-stroke MRI not only delineates focal lesions but also reveals secondary structural changes, such as atrophy and ventricular enlargement. These abnormalities, increasingly recognised as imaging biomarkers of recovery and outcome, remain poorly captured by supervised segmentation methods. We evaluate REFLECT, a flow-based generative model, for unsupervised detection of both focal and non-lesional abnormalities in post-stroke patients. Using dual-expert central-slice annotations on ATLAS data, performance was assessed at the object level with Free-Response ROC analysis for anomaly maps. Two models were trained on lesion-free slices from stroke patients (ATLAS) and on healthy controls (IXI) to test the effect of training data. On ATLAS test subjects, the IXI-trained model achieved higher lesion segmentation (Dice = 0.37 vs 0.27) and improved sensitivity to non-lesional abnormalities (FROC = 0.62 vs 0.43). Training on fully healthy anatomy improves the modelling of normal variability, enabling broader and more reliable detection of structural abnormalities.

cs.CV↗

arXiv2025

Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review

Unsupervised anomaly detection (UAD) based on deep generative modelling has been increasingly explored for identifying pathological brain abnormalities without requiring voxel-level annotations. By learning the distribution of healthy anatomy and generating pseudo-healthy reconstructions, these methods aim to localise deviations in a pathology-agnostic manner. Despite rapid methodological development - from autoencoders and variational autoencoders to generative adversarial networks and diffusion-based models - a structured synthesis of their application in structural neuroimaging is lacking. We conducted a PRISMA-ScR-guided scoping review of studies published between January 2018-December 2025 that applied unsupervised deep generative models to anomaly detection in brain MRI (and, less frequently, CT). Thirty-three studies met inclusion criteria. Methods were categorised by architectural family, and reported performance was synthesised across major pathology groups, with segmentation (Dice) and detection metrics (AUROC, AUPRC) disaggregated by evaluation level (voxel, slice, subject). For transparency, we also summarised dataset characteristics, dimensionality (2D vs. 3D), and thresholding strategies. Overall, unsupervised generative approaches demonstrate potential for pathology-agnostic anomaly localisation, particularly in settings where annotated data are scarce. However, methodological heterogeneity, limited external validation, and sensitivity to dataset characteristics remain important challenges. Emerging paradigms - including anatomy-aware modelling, diffusion-based frameworks, and alternative normative evaluation metrics - seek to address these limitations and improve robustness and clinical relevance.

cs.CV↗