본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

Computers in biology and medicine v.95, 2018년, pp.24 - 33   SCI SCIE SCOPUS
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage

Abidin, Anas Z.    (Department of Biomedical Engineering, University of Rochester Medical Center, Rochester, NY, USA   ); Deng, Botao    (Department of Electrical Engineering, University of Rochester Medical Center, Rochester, NY, USA   ); DSouza, Adora M.    (Department of Electrical Engineering, University of Rochester Medical Center, Rochester, NY, USA   ); Nagarajan, Mahesh B.    (Department of Radiological Sciences, University of California Los Angeles, Los Angeles, USA   ); Coan, Paola    (European Synchrotron Radiation Facility, Grenoble, France   ); Wismüller, Axel    (Department of Biomedical Engineering, University of Rochester Medical Center, Rochester, NY, USA  );
  • 초록  

    Abstract Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated to be effective for visualization of the human cartilage matrix at micrometer resolution, thereby capturing osteoarthritis induced changes to chondrocyte organization. This study aims to systematically assess the efficacy of deep transfer learning methods for classifying between healthy and diseased tissue patterns. We extracted features from two different convolutional neural network architectures, CaffeNet and Inception-v3 for characterizing such patterns. These features were quantitatively evaluated in a classification task measured by the area (AUC) under the Receiver Operating Characteristic (ROC) curve as well as qualitative visualization through a dimension reduction approach t-Distributed Stochastic Neighbor Embedding (t-SNE). The best classification performance, for CaffeNet, was observed when using features from the last convolutional layer and the last fully connected layer (AUCs > 0.91 ). Meanwhile, off-the-shelf features from Inception-v3 produced similar classification performance (AUC > 0.95 ). Visualization of features from these layers further confirmed adequate characterization of chondrocyte patterns for reliably distinguishing between healthy and osteoarthritic tissue classes. Such techniques, can be potentially used for detecting the presence of osteoarthritis related changes in the human patellar cartilage. Highlights Phase Contrast Imaging allows visualization of osteoarthritic changes in the patellar cartilage. Features from pre-trained CNNs can be used characterize healthy and diseased patterns. Features from Inception-v3 perform better than CaffeNet and GLCM at the classification task. Fine-tuning with a small dataset can improve classifier performance. Visualization techniques can help further substantiate the characterization obtained.


  • 주제어

    Phase contrast imaging .   Patellar cartilage .   Deep transfer learning .   Convolutional neural network.  

 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • 원문이 없습니다.

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기