arXiv Computer Vision

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.

By Linus Britt, Maximilian Nielsen, Susan Klapproth, Andre Kemmling, Michael H. Lev, Gabriel Broocks, Rene Werner, Thilo Sentker

diffusion computer-vision fine-tuning

arXiv AI

Oct 9

Bi-temporal Image-driven Acute Stroke Evolution Analysis

The paper introduces a bi‑temporal framework that links admission computed tomography perfusion (CTP) data with follow‑up diffusion‑weighted MRI (DWI) outcomes in acute ischemic stroke. By extracting statistical, radiomic, and deep‑learning features from CTP and classifying tissue into six outcome‑aware regions, the authors demonstrate that CTP signatures can distinguish salvaged versus infarcted penumbra and identify non‑hypoperfused tissue that later infarcts. Cross‑dataset validation on the ISLES'24 dataset confirms consistent feature‑space separations, suggesting that admission CTP contains richer outcome information than traditional core‑penumbra delineation.

By Md Sazidur Rahman, Kjersti Engan, Kathinka D{\ae}hli Kurz, Mahdieh Khanmohammadi

diffusion computer-vision

arXiv AI

Jul 7

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI

arXiv:2607. 03568v1 Announce Type: cross Abstract: Objective: Accurate identification of acute ischemic infarcts on diffusion-weighted magnetic resonance imaging (DWI) is a critical prerequisite for reliable lesion quantification and effective clinical decision support in the management of cerebrovascular events.

By Hasan Ulutas, Muhammet Emin Sahin, Mustafa Fatih Erkoc, Esra Yuce, Turker Tuncer, Sengul Dogan, Serkan Kiranyaz

diffusion computer-vision benchmarks

arXiv AI

Jun 9

Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI

arXiv:2601. 20503v2 Announce Type: replace-cross Abstract: White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are key imaging biomarkers of cerebral small vessel disease (SVD) detectable on magnetic resonance imaging (MRI).

By Jesse Phitidis, Alison Q. Smithard, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Maria Vald\'es Hern\'andez

computer-vision efficiency

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.

By Maunil Shah, Vaanathi Sundaresan

computer-vision benchmarks

arXiv AI

Sep 15

Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI

arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...

By Paul-Gabriel Nicolae, Irina Georgiana Mocanu

llms computer-vision multimodal

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.

By Weidong Zhang, Yongchan Jung, Shafayat Mowla Anik, Furen Xiao, Vasudevan Janarthanan, Enkhzaya Chuluunbaatar, Byeong Kil Lee, Jeeho Ryoo

diffusion computer-vision benchmarks

arXiv Computer Vision

Oct 8

Research on Deep Learning-Based Semantic Segmentation Algorithms for Subcortical Brain Structures

arXiv:1907.09194v3 Announce Type: replace-cross Abstract: Segmentation of subcortical brain structures is fundamental to computer-aided diagnosis and treatment in neurology and related clinical field...

By Binbin Yang, Weiwei Zhang

computer-vision multimodal

arXiv Computer Vision

Oct 2

Multi-Resolution Feature Fusion U-Net for Magnetic Resonance Imaging Segmentation

arXiv:2610.00279v1 Announce Type: new Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease p...

By Eirini Cholopoulou, Dimitrios E. Diamantis, Dimitris K. Iakovidis

computer-vision benchmarks

arXiv AI

Jun 2

ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI

arXiv:2606. 01293v1 Announce Type: cross Abstract: Accurate segmentation of fetal brain tissues in Magnetic Resonance Imaging (MRI) is critical for early diagnosis of congenital abnormalities and improving prenatal care.

By Ashiqur Rahman, Muhammad E. H. Chowdhury, Md. Abu Sayed, Md. Sharjis Ibne Wadud, Abu Naser Md. Arafat, Mehedi Hasan Prince

computer-vision

arXiv AI

Jun 30

A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment

arXiv:2606. 29106v1 Announce Type: cross Abstract: Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential.

By Ali Fatahi, Hoda Zamani, Mohammad H. Nadimi-Shahraki

arXiv Computer Vision

Aug 26

Multi-Stage Prompt-Guided Feature Modulation for Generalizable Brain Tumor Segmentation

arXiv:2608.23745v1 Announce Type: cross Abstract: Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and diseas...

By Mohammad Mahdi Danesh Pajouh, Sara Saeedi

computer-vision

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