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Brain tumor image segmentation method using hybrid attention module and improved mask RCNN.

Jinglin Yuan1

  • 1School of Applied Science, Macao Polytechnic University, Macau, 999078, China. p2316169@mpu.edu.mo.

Scientific Reports
|September 4, 2024
PubMed
Summary

This study enhances brain tumor segmentation in MRI using an improved mask region-based convolutional neural network with attention mechanisms. The new model offers more precise tumor localization for medical analysis.

Keywords:
Brain tumorsConvolutional neural networkDiagnostic informationFeature pyramid networkMagnetic resonance imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuro-oncology

Background:

  • Automated analysis of brain tumor magnetic resonance imaging (MRI) is crucial for efficient medical diagnosis.
  • Existing methods may lack the precision required for detailed tumor characterization.

Purpose of the Study:

  • To develop an enhanced instance segmentation method for precise brain tumor segmentation in MRI.
  • To improve the accuracy and efficiency of automated brain tumor analysis.

Main Methods:

  • An enhanced mask region-based convolutional neural network (R-CNN) was developed.
  • Incorporated squeeze-and-excitation networks (channel attention) and concatenated attention neural network (spatial attention).
  • Utilized Residual Network-50 with an attention module and feature pyramid network as the backbone, alongside region proposal network and region of interest alignment.

Main Results:

  • The enhanced model achieved a precision of 90.72% (0.76% increase).
  • Recall was 91.68% (0.95% increase), and Mean Intersection over Union (IoU) was 94.56% (1.39% increase).
  • Demonstrated improved feature extraction efficiency for brain tumors.

Conclusions:

  • The proposed method achieves precise segmentation of brain tumors in MRI.
  • Facilitates accurate measurement of tumor dimensions, aiding in comprehensive diagnostic information for clinicians.