Clinical application of mammogram microcalcification detection model based on Attention U-Net(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第6期
- Page:
- 716-723
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Clinical application of mammogram microcalcification detection model based on Attention U-Net
- Author(s):
- SUN Xiaoqi1; 2; CAI Siqing2; REN Yannan2
- 1. Department of Imaging, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China 2. Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
- Keywords:
- Keywords: mammogram microcalcification artificial intelligence breast density
- PACS:
- R318；R816.4
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.06.009
- Abstract:
- Abstract: Objective To develop a mammogram microcalcification detection model (DL model) based on Attention U-Net for realizing the efficient detection of microcalcifications, and to investigate the effects of breast density and microcalcification type on the microcalcification detection performance of the DL model. Methods A retrospective analysis was performed on 694 images from 347 patients undergoing mammography. Through the independent image diagnosis by junior physicians and review by senior physicians, the reference standard for microcalcification detection was established. Neural network training was performed to establish a DL model. The performance of the model for microcalcification detection was evaluated using precision rate, recall rate, intersection over union (IoU) and F1-score which were calculated based on calcification area or quantity and the effects of microcalcification type (benign vs malignant) and breast density (a+b vs c+d) on the model performance were also analyzed. Results For detecting microcalcifications by the DL model, the precision rate, recall rate, IoU and F1-score were 85.12%±18.39%, 78.18%±19.25%, 68.29%±21.39% and 78.96%±17.70% when the calculation was based on calcification area, and those were 76.72%±19.85%, 85.12%±18.39%, 67.13%±23.84% and 77.65%±9.37% when the calculation was based on calcification quantity. The differences in precision rate, recall rate, IoU, F1-score of DL model in different microcalcification types (benign vs malignant) and breast densities (a+b vs c+d) were insignificant. Conclusion The developed mammogram microcalcification detection model based on Attention U-Net can effectively detect breast microcalcifications and is conducive to the quantitative research on breast microcalcifications. Meanwhile, the model exhibits high stability, and the breast density and microcalcification type have trivial effects on the microcalcification detection performance of the model.
Last Update: 2024-06-25