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Purpose <p>Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was developed using preoperative MRI to assist clinicians in diagnosing pituitary microadenomas.</p> Method <p>Sixty patients with pathologically diagnosed pituitary microadenomas, including hyperprolactinemia (n = 19), growth hormone microadenomas (n = 17), and adrenocorticotropin microadenomas (n = 24), were enrolled. An image edge-supervised same receptive field semantic segmentation network was developed based on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images.</p> Results <p>The mean Intersection over Unions of our neural network model were 0.7013 ± 0.3400, 0.7295 ± 0.321, and 0.8053 ± 0.3052 for the test sets of T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences, respectively, while the Dice Similarity Coefficient values were 0.8075 ± 0.3895, 0.8192 ± 0.3733, and 0.8860 ± 0.3443 for the corresponding sequences. The performance on contrast-enhanced T1-weighted images was better than that of the other two MR sequences.</p> Conclusions <p>The image edge-supervised same receptive field segmentation network can potentially be used to precisely identify pituitary microadenomas automatically with preoperative MRI. The developed model exhibited good performance with contrast-enhanced T1-weighted images and could help neurosurgeons accurately determine the locations of pituitary microadenomas.</p>

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A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI

  • 期刊论文

  • 发表日期：

    2025年4月4日

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  • ChenGang Yuan,
  • Hang Qu,
  • HuMing Dai,
  • HaiXiao Jiang,
  • DeMao Cao,
  • LiYing Shao,
  • LiangXue Zhou,
  • AiJun Peng

摘要

Purpose

Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was developed using preoperative MRI to assist clinicians in diagnosing pituitary microadenomas.

Method

Sixty patients with pathologically diagnosed pituitary microadenomas, including hyperprolactinemia (n = 19), growth hormone microadenomas (n = 17), and adrenocorticotropin microadenomas (n = 24), were enrolled. An image edge-supervised same receptive field semantic segmentation network was developed based on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images.

Results

The mean Intersection over Unions of our neural network model were 0.7013 ± 0.3400, 0.7295 ± 0.321, and 0.8053 ± 0.3052 for the test sets of T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences, respectively, while the Dice Similarity Coefficient values were 0.8075 ± 0.3895, 0.8192 ± 0.3733, and 0.8860 ± 0.3443 for the corresponding sequences. The performance on contrast-enhanced T1-weighted images was better than that of the other two MR sequences.

Conclusions

The image edge-supervised same receptive field segmentation network can potentially be used to precisely identify pituitary microadenomas automatically with preoperative MRI. The developed model exhibited good performance with contrast-enhanced T1-weighted images and could help neurosurgeons accurately determine the locations of pituitary microadenomas.