Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Development of wound segmentation deep learning algorithm

딥러닝을 이용한 창상 분할 알고리즘

  • Hyunyoung Kang (Department of Biomedical Engineering, Yonsei University) ;
  • Yeon-Woo Heo (Department of Dermatology, Yonsei University Wonju College of Medicine) ;
  • Jae Joon Jeon (Department of Dermatology, Yonsei University Wonju College of Medicine) ;
  • Seung-Won Jung (Department of Dermatology, Yonsei University Wonju College of Medicine) ;
  • Jiye Kim (Department of Plastic and Reconstructive Surgery, Yonsei University Wonju College of Medicine) ;
  • Sung Bin Park (Department of Precision Medicine, Yonsei University Wonju College of Medicine)
  • 강현영 (연세대학교 의공학과) ;
  • 허연우 (연세대학교 원주의과대학 피부과학교실) ;
  • 전재준 (연세대학교 원주의과대학 피부과학교실) ;
  • 정승원 (연세대학교 원주의과대학 피부과학교실) ;
  • 김지예 (연세대학교 원주의과대학 성형외과교실) ;
  • 박성빈 (연세대학교 원주의과대학 정밀의학과)
  • Received : 2024.04.10
  • Accepted : 2024.04.23
  • Published : 2024.04.30
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Abstract

Diagnosing wounds presents a significant challenge in clinical settings due to its complexity and the subjective assessments by clinicians. Wound deep learning algorithms quantitatively assess wounds, overcoming these challenges. However, a limitation in existing research is reliance on specific datasets. To address this limitation, we created a comprehensive dataset by combining open dataset with self-produced dataset to enhance clinical applicability. In the annotation process, machine learning based on Gradient Vector Flow (GVF) was utilized to improve objectivity and efficiency over time. Furthermore, the deep learning model was equipped U-net with residual blocks. Significant improvements were observed using the input dataset with images cropped to contain only the wound region of interest (ROI), as opposed to original sized dataset. As a result, the Dice score remarkably increased from 0.80 using the original dataset to 0.89 using the wound ROI crop dataset. This study highlights the need for diverse research using comprehensive datasets. In future study, we aim to further enhance and diversify our dataset to encompass different environments and ethnicities.

Keywords

Acknowledgement

본 연구는 National Research Foundation of Korea(NRF) 과제의 지원을 받아 수행하였음. (NRF-2022R1A2C2091160). 본 연구는 산업통상자원부와 한국산업기술진흥원의 "지역혁신클러스터육성(R&D, P0025442)"사업의 지원을 받아 수행된 연구결과임.

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