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A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection

Muntakim Mahmud Khan1, Muhammad E H Chowdhury2, A S M Shamsul Arefin1

  • 1Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh.

Diagnostics (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning algorithm to detect intracranial hemorrhage (ICH) on CT scans, achieving high accuracy in identifying bleeding within the skull for improved patient diagnosis and care.

Keywords:
Dice similarity coefficient (DSC)computed tomographyconvolution neural networkdeep learningintersection over union (IoU)intracranial hemorrhage

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurosurgery

Background:

  • Intracranial hemorrhage (ICH) presents diagnostic challenges due to varied severity and morphology, risking missed diagnoses of small hemorrhages.
  • Computed tomography (CT) is the standard for diagnosing ICH, enabling rapid, life-saving interventions.
  • Accurate and timely detection of ICH is critical due to high mortality and disability rates.

Purpose of the Study:

  • To develop and evaluate a machine learning algorithm for detecting intracranial hemorrhage (ICH) using plain CT images.
  • To compare the performance of different deep learning models for hemorrhage segmentation.

Main Methods:

  • CT images from 75 patients were preprocessed using brain windowing, skull-stripping, and image inversion.
  • Hemorrhage segmentation was performed using U-Net, U-Net++, and Feature Pyramid Network (FPN) models.
  • A U-Net model with a DenseNet201 encoder demonstrated superior performance.

Main Results:

  • The U-Net model with DenseNet201 achieved the highest Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) scores.
  • A 3D brain model was generated to visualize predicted hemorrhages against ground truth.
  • Volumetric measurements of hemorrhage size were performed.

Conclusions:

  • The developed machine learning algorithm shows promise for accurate ICH detection in clinical practice.
  • The U-Net model with DenseNet201 encoder is effective for hemorrhage segmentation on CT scans.
  • 3D visualization and volumetric analysis aid in assessing ICH severity.