期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 171, 期 -, 页码 27-37出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2019.02.006
关键词
Convolutional neural networks; Calcaneus fracture; Computed tomography image; Residual network; Visual geometry group
类别
资金
- Ministry of Science and Technology of Taiwan [MOST106-2410-H-008-039-MY2, MOST106-2218-E-008-002]
- Ministry of Science and Technology through Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan [108-2634-F-008-004]
Background and objectives: The calcaneus is the most fracture-prone tarsal bone and injuries to the surrounding tissue are some of the most difficult to treat. Currently there is a lack of consensus on treatment or interpretation of computed tomography (CT) images for calcaneus fractures. This study proposes a novel computer-assisted method for automated classification and detection of fracture locations in calcaneus CT images using a deep learning algorithm. Methods: Two types of Convolutional Neural Network (CNN) architectures with different network depths, a Residual network (ResNet) and a Visual geometry group (VGG), were evaluated and compared for the classification performance of CT scans into fracture and non-fracture categories based on coronal, sagittal, and transverse views. The bone fracture detection algorithm incorporated fracture area matching using the speeded-up robust features (SURF) method, Canny edge detection, and contour tracing. Results: Results showed that ResNet was comparable in accuracy (98%) to the VGG network for bone fracture classification but achieved better performance for involving a deeper neural network architecture. ResNet classification results were used as the input for detecting the location and type of bone fracture using SURF algorithm. Conclusions: Results from real patient fracture data sets demonstrate the feasibility using deep CNN and SURF for computer-aided classification and detection of the location of calcaneus fractures in CT images. (C) 2019 Elsevier B.V. All rights reserved.
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