Article

Automated Detection of Lacunes in Brain MR Images Using SAM with Robust Prompts via Self-Distillation and Anatomy-Informed Priors

2025-07-10

Abstract excerpt

Lacunes, which are small fluid-filled cavities in the brain, are signs of cerebral small vessel disease and have been clinically associated with various neurodegenerative and cerebrovascular diseases. Hence, accurate detection of lacunes is crucial and is one of the initial steps for the precise diagnosis of these diseases. However, developing a robust and consistently reliable method for detecting lacunes is chal...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 03767bbf-3dd4-5ad5-b864-60daaa87bb69
DOI: 10.1101/2025.07.09.25331177

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Automated Detection of Lacunes in Brain MR Images Using SAM with Robust Prompts via Self-Distillation and Anatomy-Informed PriorsDOI 10.1101/2025.07.09.25331177Read

SHIVA-CMB: A Deep-Learning-based Robust Cerebral Microbleed Segmentation Tool Trained on Multi-Source T2*GRE- and Susceptibility- weighted MRISemantic proximity Read

A geometric-to-neural cascade for cerebral microbleed detection in susceptibility-weighted MRISemantic proximity Read

Automated Detection of Cerebral Microbleeds on MR images using Knowledge Distillation FrameworkSemantic proximity Read

Exploiting XAI maps to improve MS lesion segmentation and detection in MRISemantic proximity Read

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic strokeSemantic proximity Read

Deep learning based on susceptibility-weighted MR sequence for detecting cerebral microbleeds and classifying cerebral small vessel diseaseSemantic proximity Read

Diagnostic Accuracy and External Validation of Self-Supervised Learning for Cerebral Micro-Bleed Detection: A Multi-Sequence MRI Trial Using Public DatasetsSemantic proximity Read

Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic ReviewSemantic proximity Read

Automated Detection of Candidate Subjects with Cerebral Microbleeds using Machine LearningSemantic proximity Read

Automated quantification of cerebral microbleeds for ARIA-H monitoring in Aging and Alzheimer’s Disease: A multicenter deep learning validationSemantic proximity Read

Interpretable surface-based detection of focal cortical dysplasias: a MELD studySemantic proximity Read

segcsvd<sub>PVS</sub>: A convolutional neural network-based tool for quantification of enlarged perivascular spaces (PVS) on T1-weighted imagesSemantic proximity Read