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Related Concept Videos

Ischemic Stroke l: Introduction01:15

Ischemic Stroke l: Introduction

44
Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.
44
Ischemic Stroke ll: Pathophysiology01:15

Ischemic Stroke ll: Pathophysiology

54
An ischemic stroke occurs when a cerebral blood vessel becomes obstructed, most often by a thrombus or embolus, interrupting the delivery of oxygen and glucose to brain tissue. Because neurons rely on continuous aerobic metabolism, energy failure begins within minutes of reduced perfusion. The region receiving the least blood flow becomes the infarct core, an area of irreversible cellular death. Surrounding this core lies the penumbra, a zone of hypoperfused but still viable tissue that is...
54
Hemorrhagic Stroke l: Introduction01:17

Hemorrhagic Stroke l: Introduction

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A hemorrhagic stroke is an acute neurological event that occurs when a weakened cerebral blood vessel ruptures, allowing blood to accumulate within or around the brain. The sudden release of blood forms a focal hematoma that increases intracranial pressure, displaces neural tissue, and can obstruct cerebrospinal fluid pathways. These effects may be compounded by intraventricular extension of the hemorrhage, cerebral edema, or compression of adjacent structures, all of which contribute to...
20

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Related Experiment Video

Updated: Apr 30, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Lesion segmentation from multimodal MRI using random forest following ischemic stroke.

Jhimli Mitra1, Pierrick Bourgeat1, Jurgen Fripp1

  • 1CSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, QLD, Australia.

Neuroimage
|May 6, 2014
PubMed
Summary

This study introduces an automated method to identify brain lesions after stroke using MRI scans. The approach accurately maps infarcts and white matter changes, aiding stroke research and patient management.

Keywords:
Chronic strokeFLAIR MRIIschemic infarctLesion likelihoodMarkov random fieldRandom forestSecondary lesionsWhite matter lesions

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Area of Science:

  • Neuroimaging and computational neuroscience.
  • Advanced image analysis for neurological disorders.

Background:

  • Accurate brain lesion delineation is crucial for understanding stroke's impact on brain function.
  • Existing methods struggle to precisely identify chronic infarcts and associated white matter pathologies.

Purpose of the Study:

  • To develop and validate an automated approach for segmenting chronic ischemic infarcts and white matter hyperintensities post-stroke.
  • To improve the accuracy of lesion volume assessment for brain-behavior relationship studies.

Main Methods:

  • Utilized Bayesian-Markov Random Field (MRF) classification for initial lesion segmentation on FLAIR MRI.
  • Employed random forest classification with multimodal MRI data (T1w, T2w, FLAIR, ADC) and contextual features.
  • Final lesion segmentation achieved by thresholding probabilistic maps generated by the random forest classifier.

Main Results:

  • The automated method demonstrated a mean sensitivity of 0.53±0.13 and a positive predictive value of 0.75±0.18.
  • High correlation (r=0.76, p<0.0001) observed between automated and manual lesion volume measurements.
  • Achieved a mean Dice similarity coefficient of 0.60±0.12 for lesion overlap and a mean surface distance of 3.06mm±3.17mm.

Conclusions:

  • The automated approach successfully identifies significant lesion areas on FLAIR MRI with a low false positive rate.
  • This method aids in the robust definition of stroke lesions, supporting research into post-stroke brain-behavior relationships.
  • The developed tool can assist in developing improved post-stroke management and treatment strategies.