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MR Image Classification for Brain Tumor Texture Based on Pseudo-Label Learning and Optimized Feature Extraction.

Qianqian Xu1, Huachang Xu1, Jie Liu1

  • 1School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China.

Computational and Mathematical Methods in Medicine
|April 14, 2022
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Summary
This summary is machine-generated.

This study presents an automated method for diagnosing brain tumor texture using uneven MRI data. The approach achieves high accuracy, improving efficiency for surgical planning and prognosis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors are challenging to treat, necessitating accurate preoperative classification.
  • Pituitary tumor texture analysis is crucial for surgical planning and prognosis.
  • Current methods for brain tumor texture diagnosis lack efficiency and accuracy due to manual intervention.

Purpose of the Study:

  • To develop an automatic brain tumor texture diagnosis method for uneven sequence image data.
  • To enhance the efficiency and accuracy of preoperative classification for pituitary tumors.
  • To provide a reliable basis for surgical plan selection and prognosis prediction.

Main Methods:

  • Utilized CycleGAN for data conversion to address uneven or missing T1 and T2 MRI sequences.
  • Employed texture analysis combined with pseudo-label learning for data labeling.
  • Implemented an improved U-Net model with CBAM for optimized feature extraction.
  • Applied a CRNN model for classifying pituitary tumor texture based on sequence data advantages.

Main Results:

  • Successfully generated complete MRI spatial sequences from uneven or missing data.
  • Achieved accurate labeling of pituitary tumor data using texture analysis and pseudo-label learning.
  • Optimized feature extraction for pituitary tumor images through an improved U-Net model.
  • Classified pituitary tumor texture with high efficiency and an accuracy rate of 94.23%.

Conclusions:

  • The proposed automatic method significantly improves the efficiency of brain tumor texture diagnosis.
  • The approach provides accurate preoperative classification, aiding surgical planning and prognosis.
  • This technique offers a robust solution for analyzing uneven MRI sequence data in neuro-oncology.