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Related Experiment Video

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Intracranial hemorrhage segmentation and classification framework in computer tomography images using deep learning

S Nafees Ahmed1, P Prakasam2

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.

Scientific Reports
|May 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI framework for segmenting and classifying intracranial hemorrhages from CT scans, improving diagnostic accuracy and aiding neurosurgical treatment strategies for better patient survival rates.

Keywords:
ClassificationDeep learningIntracranial hemorrhageMUNETSegmentation

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine
  • Neuroscience

Background:

  • Medical image segmentation is crucial for neurosurgery, but manual analysis is time-consuming and prone to errors.
  • Automated techniques are vital for improving the efficiency and accuracy of hemorrhage detection in Computed Tomography (CT) scans.

Purpose of the Study:

  • To propose a novel framework, IHSNet (Intracranial Hemorrhage Segmentation and Classification Framework), for accurate segmentation and classification of multiple intracranial hemorrhage subtypes.
  • To leverage a Multiclass-UNet (MUNet) architecture for enhanced performance in medical image analysis.

Main Methods:

  • Development of the IHSNet framework utilizing MUNet for multi-class hemorrhage segmentation.
  • Integration of fully connected layers within the framework for hemorrhage classification.
  • Evaluation of the proposed method on various intracranial hemorrhage subtypes: IVH, EDH, IPH, SDH, and SAH.

Main Results:

  • Achieved an overall hemorrhage segmentation accuracy of 98.53% and classification accuracy of 98.71%.
  • Specific DICE coefficients for subtypes: IVH (0.77), EDH (0.84), IPH (0.64), SDH (0.80), and SAH (0.92).
  • Demonstrated high performance in segmenting and classifying multiple types of intracranial hemorrhages.

Conclusions:

  • The proposed IHSNet framework shows significant potential for computer-aided diagnostics in neurosurgery.
  • The method offers a robust solution for medical image segmentation, with future scope for broader applications.
  • Improved accuracy in hemorrhage detection can aid in developing effective treatment strategies and increase patient survival rates.