[1]孙晓琪,蔡思清,任艳楠.基于Attention U-Net的乳腺X线图像微钙化检测模型的临床应用[J].中国医学物理学杂志,2024,41(6):716-723.[doi:DOI:10.3969/j.issn.1005-202X.2024.06.009]
　SUN Xiaoqi,CAI Siqing,REN Yannan.Clinical application of mammogram microcalcification detection model based on Attention U-Net[J].Chinese Journal of Medical Physics,2024,41(6):716-723.[doi:DOI:10.3969/j.issn.1005-202X.2024.06.009]
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基于Attention U-Net的乳腺X线图像微钙化检测模型的临床应用()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第6期
页码:
716-723
栏目:
医学影像物理
出版日期:
2024-06-25

文章信息/Info

Title:
Clinical application of mammogram microcalcification detection model based on Attention U-Net
文章编号:
1005-202X（2024）06-0716-08
作者:
孙晓琪1; 2; 蔡思清2; 任艳楠2
1.福建医科大学附属泉州第一医院影像科， 福建 泉州362000； 2.福建医科大学附属第二医院放射科， 福建 泉州362000
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
关键词:
乳腺X线图像; 微钙化; 人工智能; 乳腺密度
Keywords:
Keywords: mammogram microcalcification artificial intelligence breast density
分类号:
R318；R816.4
DOI:
DOI:10.3969/j.issn.1005-202X.2024.06.009
文献标志码:
A
摘要:
目的：通过开发基于Attention U-Net的乳腺X线图像微钙化检测模型，实现微钙化的高效率检出，并探究不同性质钙化、不同乳腺密度对该深度学习模型微钙化检测性能的影响。方法：回顾性分析接受乳腺常规X线检查的347例患者的694幅图像。通过低年资医师独立阅片，高年资医师审核的方式，建立微钙化检出的参考标准。进行神经网络训练，建立深度学习模型。以钙化面积和数量分别计算，并采用精确率、召回率、F1分数、交并比等指标评估微钙化检测性能，分析不同性质钙化（良性vs恶性）、不同乳腺密度（a+b类vs c+d类）对深度学习模型微钙化检测性能的影响。结果：深度学习模型对微钙化检测的精确率为85.12%±18.39%（以钙化面积计算）和76.72%±19.85%（以钙化数量计算）；召回率为78.18%±19.25%（以钙化面积计算）和85.12%±18.39%（以钙化数量计算）；交并比为68.29%±21.39%（以钙化面积计算）和67.13%±23.84%（以钙化数量计算）；F1分数为78.96%±17.70%（以钙化面积计算）和77.65%±9.37%（以钙化数量计算）。深度学习模型在不同钙化性质（良性vs恶性）中的精确率、召回率、交并比、F1分数之间差异均无统计学意义（P>0.05），在不同乳腺密度（a+b类vs c+d类）中对微钙化检测的精确率、召回率、交并比、F1分数之间差异无统计学意义（P>0.05）。结论：基于Attention U-Net的乳腺X线图像微钙化检测模型能够对乳腺微钙化进行有效的检测、有助于乳腺微钙化的定量研究，同时该模型稳定性强，钙化性质及乳腺密度对该模型的检测性能无影响。
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.

相似文献/References:

[1]程运福,张光玉,崔栋,等.基于乳腺X线图像的微钙化点区域自动检测算法研究[J].中国医学物理学杂志,2013,30(02):3992.[doi:10.3969/j.issn.1005-202X.2013.02.007]
[2]申楠,邢素霞,何湘萍,等.基于Adaboost-决策树算法的乳腺微钙化区域真假阳性检测[J].中国医学物理学杂志,2021,38(8):940.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.004]
　SHEN Nan,XING Suxia,HE Xiangping,et al.True- and false-positive detections of breast microcalcifications based on Adaboost-decision tree algorithm[J].Chinese Journal of Medical Physics,2021,38(6):940.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.004]
[3]孙晓琪,蔡思清,林舒婷,等.乳腺断层摄影合成技术的临床价值[J].中国医学物理学杂志,2022,39(1):51.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.009]
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备注/Memo

备注/Memo:
【收稿日期】2024-01-08 【基金项目】福建省自然科学基金（2021J01257）；吴阶平医学基金（3206750.2021-06-35） 【作者简介】孙晓琪，硕士，医师，研究方向：乳腺疾病的影像诊断，E-mail: 812157370@qq.com 【通信作者】蔡思清，副教授，主任医师，研究方向：乳腺疾病、骨质疏松等，E-mail: 1920455696@qq.com
更新日期/Last Update: 2024-06-25