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Related Experiment Video

Updated: Feb 20, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

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Segmentation of hyper-acute cerebral infarct based on random forest and sparse coding from diffusion weighted

Xiaodong Zhang, Ahmed Elazab, Qingmao Hu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary

    Accurately segmenting hyper-acute ischemic stroke infarcts is crucial for treatment decisions. This study introduces a novel method using random forest and sparse coding, achieving improved infarct segmentation accuracy to aid thrombolytic therapy.

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    Area of Science:

    • Neuroimaging
    • Medical Image Analysis
    • Stroke Research

    Background:

    • Accurate segmentation of irreversible infarcts is vital for assessing risks and benefits of thrombolysis in hyper-acute ischemic stroke.
    • Segmenting infarcts at the hyper-acute stage is challenging due to significant variability in presentation.

    Purpose of the Study:

    • To propose and validate a general abnormal tissue segmentation method for hyper-acute ischemic infarcts.
    • To improve the accuracy of infarct quantification in the early stages of ischemic stroke.

    Main Methods:

    • A random forest classifier trained on multiple features for voxel classification.
    • Sparse coding-based bag-of-features for infarct region recognition.
    • Validation on 98 consecutive patients within 6 hours of stroke onset.

    Main Results:

    • The proposed method achieved a Dice coefficient of 0.774±0.117.
    • This performance was superior to two existing methods (0.755±0.118 and 0.597±0.204).
    • Demonstrated accurate quantification of infarcts from diffusion-weighted imaging.

    Conclusions:

    • The developed method offers a potential tool for accurate infarct quantification in hyper-acute ischemic stroke.
    • Improved infarct segmentation can assist in clinical decision-making, particularly for thrombolytic therapy.
    • Highlights the utility of advanced machine learning techniques in neuroimaging analysis.