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ICUnet++: an Inception-CBAM network based on Unet++ for MR spine image segmentation.

Lei Li1, Juan Qin1, Lianrong Lv1

  • 1School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin, 300384 China.

International Journal of Machine Learning and Cybernetics
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces ICUnet++, an efficient automatic segmentation model for MR spine images. It significantly improves segmentation accuracy for vertebrae and intervertebral discs, aiding clinical diagnosis.

Keywords:
Attention mechanismConvolutional neural networkDeep learningMRISpine segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Spine Surgery

Background:

  • Spinal diseases necessitate accurate medical image segmentation for diagnosis and treatment.
  • Traditional segmentation methods are time-consuming and labor-intensive.
  • Automated segmentation of vertebrae and intervertebral discs is crucial for efficient clinical evaluation.

Purpose of the Study:

  • To develop an efficient and novel automatic segmentation network model for MR spine images.
  • To improve the accuracy and speed of spinal image segmentation compared to traditional methods.
  • To introduce the Inception-CBAM Unet++ (ICUnet++) model for enhanced feature extraction and attention-based refinement.

Main Methods:

  • Designed the Inception-CBAM Unet++ (ICUnet++) model, integrating Inception modules for multi-receptive field feature extraction.
  • Incorporated Attention Gate and CBAM modules to enhance local feature highlighting.
  • Evaluated the model using the SpineSagT2Wdataset3 spinal MRI dataset.
  • Utilized Intersection over Union (IoU), Dice Similarity Coefficient (DSC), True Positive Rate (TPR), and Positive Predictive Value (PPV) for performance assessment.

Main Results:

  • The ICUnet++ model achieved high segmentation performance.
  • Achieved IoU of 83.16%, DSC of 90.32%, TPR of 90.40%, and PPV of 90.52%.
  • Demonstrated significant improvements in segmentation indicators compared to existing methods.

Conclusions:

  • The proposed ICUnet++ model is effective for automatic segmentation of MR spine images.
  • The model enhances feature extraction and attention mechanisms for improved accuracy.
  • This advancement facilitates quicker and more accurate clinical evaluation of spinal diseases.