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相关实验视频

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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一个基于深度学习的自动光度分析软件,用于乳房美学评分.

Joseph Kyu-Hyung Park1, Seungchul Baek1, Chan Yeong Heo1

  • 1Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnamsi, Gyeonggi-do, Republic of Korea.

Archives of plastic surgery
|March 1, 2024
PubMed
概括

首尔乳房美学评分工具 (S-BEST) 使用深度学习从2D照片进行自动化乳房美学评估. 它准确地测量了地标和不对称性,提供了可靠的临床和研究工具.

关键词:
美学 审美学 审美学乳腺癌 乳腺癌 乳腺癌深度学习是一种深度学习.

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科学领域:

  • 医学成像医学成像
  • 计算机视觉 计算机视觉 计算机视觉
  • 整形手术 整形手术 整形手术

背景情况:

  • 主观的乳房美学评估需要客观的,自动化的工具.
  • 首尔乳房美学评分工具 (S-BEST) 是为光度分析而开发的.
  • S-BEST使用DenseNet-264深度学习模型进行里程碑和不对称性评估.

研究的目的:

  • 开发和验证一个自动化的软件工具,用于客观的乳房美学评估.
  • 评估S-BEST在测量乳腺标志和不对称指数方面的准确性.
  • 为了将S-BEST测量与体检数据进行比较.

主要方法:

  • 训练一个DenseNet-264模型从前部乳房照片上的30个注释地标上.
  • 实现图像预处理,包括比率校正和规范化.
  • 在100名女性乳腺癌患者的物理测量中验证S-BEST的准确性.

主要成果:

  • 在自动地标定位方面,S-BEST实现了高精度,与物理测量相比,统计差异最小.
  • 乳头到乳下折叠距离显示出显著的偏差.
  • 乳头到乳房内膜折叠距离的确定系数在0.3787到0.4234.4之间.

结论:

  • S-BEST提供了一种快速,可靠,自动化的方法,用于使用2D正面图像进行乳房美学评估.
  • 该工具可用于临床和研究应用.
  • 局限性包括无法评估体积数据或多个视角.