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A Novel, Deep Learning-Based, Automatic Photometric Analysis Software for Breast Aesthetic Scoring.

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

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The Seoul Breast Esthetic Scoring Tool (S-BEST) uses deep learning for automated breast aesthetic evaluation from 2D photos. It accurately measures landmarks and asymmetry, offering a reliable clinical and research tool.

Keywords:
aestheticsbreast cancerdeep learning

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Area of Science:

  • Medical imaging
  • Computer vision
  • Plastic surgery

Background:

  • Subjective breast aesthetics assessment necessitates objective, automated tools.
  • The Seoul Breast Esthetic Scoring Tool (S-BEST) was developed for photometric analysis.
  • S-BEST employs a DenseNet-264 deep learning model for landmark and asymmetry evaluation.

Purpose of the Study:

  • To develop and validate an automated software tool for objective breast aesthetic evaluation.
  • To assess the accuracy of S-BEST in measuring breast landmarks and asymmetry indices.
  • To compare S-BEST measurements with physical examination data.

Main Methods:

  • Training a DenseNet-264 model on 30 annotated landmarks from frontal breast photographs.
  • Implementing image preprocessing including ratio correction and normalization.
  • Validating S-BEST accuracy against physical measurements in 100 female breast cancer patients.

Main Results:

  • S-BEST achieved high accuracy in automatic landmark localization with minimal statistical difference from physical measurements.
  • The nipple to inframammary fold distance showed a significant bias.
  • Coefficient of determination for nipple-to-inframammary fold distance ranged from 0.3787 to 0.4234.

Conclusions:

  • S-BEST offers a fast, reliable, automated method for breast aesthetic evaluation using 2D frontal images.
  • The tool is accessible for clinical and research applications.
  • Limitations include the inability to assess volumetric data or multiple viewpoints.