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A deep semantic segmentation correction network for multi-model tiny lesion areas detection.

Yue Liu1,2, Xiang Li1,2,3, Tianyang Li1,2

  • 1Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, China.

BMC Medical Informatics and Decision Making
|July 31, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for segmenting and detecting tiny brain lesions like focal cerebral ischemia (FCI) and lacunar infarction (LACI). The method accurately identifies and classifies these small white matter hyperintensities, aiding early disease prevention.

Keywords:
Focal cerebral ischemiaLacunar infarctMagnetic resonance imagingMulti-modalityWhite matter hyperintensities

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • Accurate segmentation of white matter hyperintensities (WMH) in focal cerebral ischemia (FCI) and lacunar infarction (LACI) is crucial for early detection and prevention.
  • Current methods struggle with segmenting small, multi-modal lesions characteristic of FCI and LACI.
  • Existing brain MRI lesion segmentation research primarily focuses on larger lesions like gliomas and acute cerebral infarction.

Purpose of the Study:

  • To develop and validate a novel segmentation correction algorithm for precise estimation of lesion areas in FCI and LACI.
  • To improve the automatic screening of tiny cerebral lesions and facilitate early prevention strategies for LACI.
  • To enhance the reliability and precision of lesion segmentation and detection in brain MRI.

Main Methods:

  • A dual-model approach was developed, comprising a segmentation network and a correction network.
  • The segmentation network extracts potential diseased areas from T2 fluid-attenuated inversion recovery (FLAIR) images.
  • The correction network classifies these areas on T1 FLAIR images to differentiate between FCI and LACI, subsequently refining segmentation results.

Main Results:

  • The proposed method achieved high precision in experimental validation on 113 clinical patient MRI scans.
  • Detection precision reached 91.76%, and classification precision reached 92.89%.
  • These results demonstrate the method's effectiveness in distinguishing small lesions like FCI and LACI.

Conclusions:

  • A comprehensive method for segmenting and detecting WMH related to FCI and LACI has been successfully developed.
  • The algorithm shows significant potential for clinical application in diagnosing and managing small cerebral lesions.
  • Future work will involve expanding the dataset and testing the method on a wider range of tiny lesion types.