期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 95, 期 -, 页码 24-33出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2018.01.008
关键词
Phase contrast imaging; Patellar cartilage; Deep transfer learning; Convolutional neural network
类别
资金
- National Institute of Health (NIH) [R01-DA-034977]
- Harry W. Fischer Award of the University of Rochester
- Clinical and Translational Science Award within the Upstate New York Translational Research Network (UNYTRN) of the Clinical and Translational Science Institute (CTSI), University of Rochester [528527]
- Center for Emerging and Innovative Sciences, a NYSTAR-designated Center for Advanced Technology
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 alms 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 osteoarthridc tissue classes. Such techniques, can be potentially used for detecting the presence of osteoarthritis related changes in the human patellar cartilage.
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