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Deep learning provides a solution to this problem, since it can effectively extract representative features from lesions and the background in <jats:styled-content style=\"fixed-case\">BUS<\/jats:styled-content> images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A novel automatic tumor segmentation method is proposed by combining a dilated fully convolutional network (<jats:styled-content style=\"fixed-case\">DFCN<\/jats:styled-content>) with a phase\u2010based active contour (<jats:styled-content style=\"fixed-case\">PBAC<\/jats:styled-content>) model. The <jats:styled-content style=\"fixed-case\">DFCN<\/jats:styled-content> is an improved fully convolutional neural network with dilated convolution in deeper layers, fewer parameters, and batch normalization techniques; and has a large receptive field that can separate tumors from background. The predictions made by the <jats:styled-content style=\"fixed-case\">DFCN<\/jats:styled-content> are relatively rough due to blurry boundaries and variations in tumor sizes; thus, the <jats:styled-content style=\"fixed-case\">PBAC<\/jats:styled-content> model, which adds both region\u2010based and phase\u2010based energy functions, is applied to further improve segmentation results. The <jats:styled-content style=\"fixed-case\">DFCN<\/jats:styled-content> model is trained and tested in dataset 1 which contains 570\u00a0<jats:styled-content style=\"fixed-case\">BUS<\/jats:styled-content> images from 89 patients. In dataset 2, a 10\u2010fold support vector machine (<jats:styled-content style=\"fixed-case\">SVM<\/jats:styled-content>) classifier is employed to verify the diagnostic ability using 460 features extracted from the segmentation results of the proposed method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Advantages of the present method were compared with three state\u2010of\u2010the\u2010art networks; the <jats:styled-content style=\"fixed-case\">FCN<\/jats:styled-content>\u20108s, U\u2010net, and dilated residual network (<jats:styled-content style=\"fixed-case\">DRN<\/jats:styled-content>). Experimental results from 170\u00a0<jats:styled-content style=\"fixed-case\">BUS<\/jats:styled-content> images show that the proposed method had a Dice Similarity coefficient of 88.97\u00a0\u00b1\u00a010.01%, a Hausdorff distance (<jats:styled-content style=\"fixed-case\">HD<\/jats:styled-content>) of 35.54\u00a0\u00b1\u00a029.70 pixels, and a mean absolute deviation (<jats:styled-content style=\"fixed-case\">MAD<\/jats:styled-content>) of 7.67\u00a0\u00b1\u00a06.67 pixels, which showed the best segmentation performance. In dataset 2, the area under curve (<jats:styled-content style=\"fixed-case\">AUC<\/jats:styled-content>) of the 10\u2010fold <jats:styled-content style=\"fixed-case\">SVM<\/jats:styled-content> classifier was 0.795 which is similar to the classification using the manual segmentation results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed automatic method may be sufficiently accurate, robust, and efficient for medical ultrasound applications.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13268","type":"journal-article","created":{"date-parts":[[2018,10,30]],"date-time":"2018-10-30T10:10:38Z","timestamp":1540894238000},"page":"215-228","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":192,"title":["Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour 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