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Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation.

Seung-Ah Lee1, Jae-Won Jang2,3,4, Sang-Won Park2,3,4

  • 1Department of Computer and Communications Engineering, Kangwon National University, Chuncheon 24253, Korea.

Journal of Personalized Medicine
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

Accurate acute ischemic stroke (AIS) volume estimation from diffusion-weighted imaging (DWI) is now possible with a novel indirect 2D segmentation model. This method offers objective, rapid, and precise assessment to aid clinical decision-making in AIS patients.

Keywords:
acute ischemic strokecomputer-aided diagnosisdeep-learningsegmentation

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Area of Science:

  • Medical Imaging
  • Neurology
  • Artificial Intelligence in Medicine

Background:

  • Accurate estimation of acute ischemic stroke (AIS) volume using diffusion-weighted imaging (DWI) is critical for patient assessment and treatment guidance.
  • Existing methods may lack objectivity, speed, or accuracy in determining AIS lesion size from DWI.
  • Developing automated tools can significantly improve the efficiency and reliability of AIS volume quantification.

Purpose of the Study:

  • To propose and validate an objective, rapid, and accurate method for estimating AIS volume from DWI.
  • To compare the performance of 2D (indirect) and 3D (direct) segmentation algorithms for AIS volume estimation.
  • To assess the clinical utility of the developed model as a decision-support tool for physicians.

Main Methods:

  • Development of algorithms using 3D segmentation (direct estimation) and 2D segmentation (indirect estimation) for AIS volume quantification.
  • Utilized a large dataset of DWI scans from 2159 participants with various AIS types for internal and external validation.
  • Compared algorithm performance against manual annotations by neurologists, evaluating segmentation metrics and volume estimation accuracy.

Main Results:

  • The pretrained indirect model (2D segmentation) outperformed the direct model (3D segmentation) in segmentation performance across internal and external validation sets.
  • The indirect model achieved high volume estimation reliability, with 93.3% volume similarity (VS) and 0.797 mean absolute error (MAE) in internal validation.
  • External validation confirmed the indirect model's robustness, showing 89.2% VS and 2.5% MAE, demonstrating its potential for real-world application.

Conclusions:

  • The indirect model utilizing 2D segmentation provides an accurate and reliable method for estimating AIS volume from DWI.
  • This automated approach can serve as a valuable supporting tool for physicians in making critical clinical decisions for AIS patients.
  • The study highlights the potential of AI-driven image analysis to enhance stroke care efficiency and accuracy.