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Related Concept Videos

Anatomy of the Brain: Ventricles01:18

Anatomy of the Brain: Ventricles

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There are hollow fluid-filled cavities known as ventricles deep inside the human brain. There are two lateral ventricles, one in each cerebral hemisphere, and each has three different projections — the anterior, inferior, and posterior horns visible from the lateral side. A thin membrane called the septum pellucidum separates the two lateral ventricles. The slender third ventricle in the diencephalon is connected to each lateral ventricle via a channel called the interventricular foramen.
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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Objective Ventricle Segmentation in Brain CT with Ischemic Stroke Based on Anatomical Knowledge.

Xiaohua Qian1, Yuan Lin2, Yue Zhao1

  • 1College of Electronic Science and Engineering, Jilin University, Changchun 130012, China.

Biomed Research International
|March 9, 2017
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Summary

This study presents a novel system for segmenting brain ventricles in computed tomography (CT) scans to improve ischemic stroke detection. The method accurately differentiates ventricles from stroke regions, enhancing diagnostic capabilities.

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Area of Science:

  • Medical Imaging
  • Neurology
  • Computer-Aided Diagnosis

Background:

  • Accurate brain ventricle segmentation in computed tomography (CT) is crucial for developing effective ischemic stroke detection systems.
  • Ischemic stroke regions often exhibit similar intensity to brain ventricles, posing a significant segmentation challenge.

Purpose of the Study:

  • To develop an objective and robust system for brain ventricle segmentation in CT scans.
  • To improve the accuracy of ischemic stroke detection by effectively excluding stroke regions from ventricle segmentation.

Main Methods:

  • Utilized clustering techniques, connectivity analysis, and domain knowledge to estimate ventricle intensity distribution.
  • Implemented a combined segmentation strategy involving 3D connected components, image difference methods with optimal thresholding, and adaptive template algorithms to exclude stroke regions.
  • Evaluated the system on 50 patient CT scans with ischemic stroke.

Main Results:

  • Achieved a mean Dice coefficient of 0.9447, indicating high overlap between segmented and actual ventricle regions.
  • Demonstrated high performance with a mean sensitivity of 0.969 and specificity of 0.998.
  • Reported a low root mean squared error of 0.219 mm, signifying precise segmentation accuracy.

Conclusions:

  • The developed system provides desirable performance for objective brain ventricle segmentation in CT.
  • This method is expected to significantly contribute to clinical research and the advancement of ischemic stroke detection systems.