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DBT Masses Automatic Segmentation Using U-Net Neural Networks.

Xiaobo Lai1, Weiji Yang2, Ruipeng Li3

  • 1College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou 310053, China.

Computational and Mathematical Methods in Medicine
|May 16, 2020
PubMed
Summary
This summary is machine-generated.

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This study presents a U-Net based algorithm for accurate automatic segmentation of breast masses in digital breast tomosynthesis (DBT) images. The method enhances mass visibility and achieves high accuracy, showing clinical potential.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Image Segmentation

Background:

  • Accurate segmentation of breast masses in digital breast tomosynthesis (DBT) is crucial for diagnosis.
  • Existing methods may struggle with background noise and contrast variations in DBT images.

Purpose of the Study:

  • To develop and evaluate a U-Net based algorithm for improving automatic segmentation accuracy of breast masses in DBT images.

Main Methods:

  • A U-Net architecture was employed for segmentation.
  • Preprocessing included top-hat transform and a constraint matrix to enhance mass contrast.
  • Data augmentation and patch extraction were used for training the U-Net model.
  • Post-processing involved removing small false positive regions and median filtering for boundary smoothing.

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Main Results:

  • The algorithm achieved high performance metrics: Accuracy (Acc) of 0.871, Sensitivity (Sen) of 0.869, Specificity (Spe) of 0.882, and Area Under the Curve (AUC) of 0.859.
  • The U-Net based system demonstrated superior performance compared to classical architectures.

Conclusions:

  • The proposed U-Net based method effectively improves automatic segmentation of breast masses in DBT images.
  • The system shows promising results and potential for clinical application in breast cancer screening and diagnosis.