arXiv Computer Vision
Native-Space 3D CarveMix for Multi-Site T1w Stroke Segmentation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Aug 24
Native-Space 3D CarveMix for Multi-Site T1w Stroke Segmentation
Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is difficult because lesions are subtle and share intensi...
arXiv Computer Vision
Aug 21
AsymFeX: A Symmetry-Driven Framework for Ischemic Stroke Segmentation Across Imaging Modalities and Stroke Stages
arXiv:2608. 19769v1 Announce Type: cross Abstract: Fast and accurate segmentation of Acute Ischemic Stroke (AIS) lesions is essential for stroke prognosis and treatment planning.
arXiv Computer Vision
Oct 2
Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings
The paper introduces a synthetic training framework for segmenting long‑tail haemorrhagic lesions such as cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS) without requiring real lesion annotations. By starting from anatomical brain parcellations, the method applies spatial augmentation, voxel resampling, and procedural insertion of lesion labels guided by clinical priors, then synthesises images with randomized intensity, blurring, and Rician noise. Models trained on these synthetic image‑label pairs outperformed classical filter baselines, achieving higher AUPRC and AUROC for both cSS and CMB segmentation.
arXiv Machine Learning
Jun 16
Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis
arXiv:2606. 15457v1 Announce Type: cross Abstract: 3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns.
arXiv Computer Vision
Aug 27
Deep Learning Segmentation of Diffusion-Weighted MRI Acute Ischaemic Stroke: A Pragmatic Evaluation Across Three Datasets
The study evaluated a pragmatic deep‑learning approach for segmenting acute ischemic stroke lesions on diffusion‑weighted MRI. Using a self‑configured nnU‑Net trained on 1,744 cases and tested on 436, the baseline model achieved a median Dice similarity coefficient of 0.84, outperforming the DeepISLES ensemble, especially for smaller infarcts. The approach required minimal preprocessing and fast inference, suggesting it could streamline clinical stroke imaging workflows.
arXiv Computer Vision
Sep 18
Ischemic Stroke Segmentation and Net Water Uptake Quantification on Multicenter Non-Contrast CT Using Supervised Target-Domain Adaptation
This study presents a domain-aware deep learning framework based on nnU-Net for segmenting ischemic stroke lesions on non‑contrast CT scans and quantifying net water uptake (NWU). Trained on data from Hamburg and the Acute Ischemic Stroke Dataset, the model was fine‑tuned on small target‑domain subsets from Boston and ISLES, achieving median Dice scores of 0.68 and 0.56 for lesions ≥30 mL, and an NWU mean absolute error of 1.37 percentage points on the Boston cohort. The results demonstrate that target‑domain adaptation can enable accurate NCCT‑only infarct segmentation and low‑error NWU estimation across heterogeneous multicenter datasets.