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deepPGSegNet: MRI-based pituitary gland segmentation using deep learning.

Uk-Su Choi1, Yul-Wan Sung2, Seiji Ogawa2

  • 1Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu, Republic of Korea.

Frontiers in Endocrinology
|February 19, 2024
PubMed
Summary

This study presents a deep learning model for automated pituitary gland segmentation in MRI scans, achieving high accuracy. This method enhances the diagnosis and monitoring of pituitary disorders.

Keywords:
3D UNetMRIdeep learningpituitary disorderspituitary glandsegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Endocrinology

Background:

  • Pituitary gland segmentation is crucial for diagnosing pituitary disorders.
  • Manual segmentation is time-consuming and prone to errors.
  • Automated segmentation using deep learning offers a promising alternative.

Purpose of the Study:

  • To develop and evaluate a deep learning model for automated pituitary gland segmentation from MRI.
  • To assess the model's performance in terms of accuracy, precision, recall, and F1 score.
  • To explore the relationship between pituitary gland morphology and age.

Main Methods:

  • A 3D U-Net architecture with data augmentation was employed.
  • A dataset of 153 university students' MRI images was used for training and validation.
  • Five-fold cross-validation and a predefined field of view were utilized for optimization.

Main Results:

  • The model achieved high performance metrics: 92.7% accuracy, 0.87 precision, 0.91 recall, and 0.89 F1 score.
  • A significant association was found between pituitary gland volume/area and age.
  • The automated segmentation enabled precise volumetric analysis.

Conclusions:

  • Automated pituitary gland segmentation using deep learning is accurate and reliable.
  • This technology can significantly improve the diagnosis and surveillance of pituitary disorders.
  • Volumetric analysis of the pituitary gland can provide valuable insights into age-related changes.