의학물리학

본문글자크기
  • [Med Phys.] A convolutional neural network-based model observer for breast CT images.인공지능을 이용한 유방 CT 영상의 모델 옵저버 연구

    연세대 / 김기훈, 심현정*, 백종덕*

  • 출처
    Med Phys.
  • 등재일
    2020 Apr
  • 저널이슈번호
    47(4):1619-1632. doi: 10.1002/mp.14072. Epub 2020 Feb 29.
  • 내용

    바로가기  >

    Abstract
    PURPOSE:
    In this paper, we propose a convolutional neural network (CNN)-based efficient model observer for breast computed tomography (CT) images.

    METHODS:
    We first showed that the CNN-based model observer provided similar detection performance to the ideal observer (IO) for signal-known-exactly and background-known-exactly detection tasks with an uncorrelated Gaussian background noise image. We then demonstrated that a single-layer CNN without a nonlinear activation function provided similar detection performance in breast CT images to the Hotelling observer (HO). To train the CNN-based model observer, we generated simulated breast CT images to produce a training dataset in which different background noise structures were generated using filtered back projection with a ramp, or a Hanning weighted ramp, filter. Circular, elliptical, and spiculated signals were used for the detection tasks. The optimal depth and the number of channels for the CNN-based model observer were determined for each task. The detection performances of the HO and a channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) and partial least squares (PLS) channels were also estimated for comparison.

    RESULTS:
    The results showed that the CNN-based model observer provided higher detection performance than the HO, LG-CHO, and PLS-CHO for all tasks. In addition, it was shown that the proposed CNN-based model observer provided higher detection performance than the HO using a smaller training dataset.

    CONCLUSIONS:
    In the presence of nonlinearity in the CNN, the proposed CNN-based model observer showed better performance than other linear observers.

     

     


    Author information

    Kim G1, Han M1, Shim H1, Baek J1.
    1
    School of Integrated Technology and Yonsei Institute of Convergence Technology, Yonsei University, 162-1, Incheon, South Korea.

  • 키워드
    breast CT images; convolutional neural network; hotelling observer; ideal observer
  • 연구소개
    본 연구는 Deep learning 기술을 이용한 model observer를 제안하였습니다. 기존의 linear function을 이용한 model observer 보다 좋은 성능을 제안하였으며, 여러 방면에서 더 좋은 성능을 보여준다는 것을 확인 및 분석하였습니다.
  • 덧글달기
    덧글달기
       IP : 3.235.180.245

    등록