융복합기술연구소 논문집 (Journal of Institute of Convergence Technology)

한국교통대학교 융복합기술연구소 (Institute of Convergence Technology)
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3차원 의료 영상의 영역 분할을 위한 효율적인 데이터 보강 방법

An Efficient Data Augmentation for 3D Medical Image Segmentation

  • 박상근 (한국교통대학교 기계공학)
  • Park, Sangkun (Department of Mechanical Engineering, Korea National University of Transportation)
  • 투고 : 2021.10.13
  • 심사 : 2021.10.28
  • 발행 : 2021.11.30
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초록

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

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과제정보

이 논문은 2021년 한국교통대학교 지원을 받아 수행하였음.

참고문헌

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