Computer-Aided Diagnosis

Global detection approach for clustered microcalcifications in mammograms using a deep learning network

[+] Author Affiliations
Juan Wang, Yongyi Yang

Illinois Institute of Technology, Medical Imaging Research Center, Department of Electrical and Computer Engineering, Chicago, Illinois, United States

Robert M. Nishikawa

University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States

J. Med. Imag. 4(2), 024501 (Apr 22, 2017). doi:10.1117/1.JMI.4.2.024501
History: Received December 27, 2016; Accepted April 6, 2017
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Abstract.  In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window (1  cm2 in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Juan Wang ; Robert M. Nishikawa and Yongyi Yang
"Global detection approach for clustered microcalcifications in mammograms using a deep learning network", J. Med. Imag. 4(2), 024501 (Apr 22, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.2.024501


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