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Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.

Abstract：

PURPOSE:Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively extract representative features from lesions and the background in BUS images. METHODS:A novel automatic tumor segmentation method is proposed by combining a dilated fully convolutional network (DFCN) with a phase-based active contour (PBAC) model. The DFCN 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 DFCN are relatively rough due to blurry boundaries and variations in tumor sizes; thus, the PBAC model, which adds both region-based and phase-based energy functions, is applied to further improve segmentation results. The DFCN model is trained and tested in dataset 1 which contains 570 BUS images from 89 patients. In dataset 2, a 10-fold support vector machine (SVM) classifier is employed to verify the diagnostic ability using 460 features extracted from the segmentation results of the proposed method. RESULTS:Advantages of the present method were compared with three state-of-the-art networks; the FCN-8s, U-net, and dilated residual network (DRN). Experimental results from 170 BUS images show that the proposed method had a Dice Similarity coefficient of 88.97 ± 10.01%, a Hausdorff distance (HD) of 35.54 ± 29.70 pixels, and a mean absolute deviation (MAD) of 7.67 ± 6.67 pixels, which showed the best segmentation performance. In dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.795 which is similar to the classification using the manual segmentation results. CONCLUSIONS:The proposed automatic method may be sufficiently accurate, robust, and efficient for medical ultrasound applications.

journal_name

Med Phys

journal_title

Medical physics

authors

Hu Y,Guo Y,Wang Y,Yu J,Li J,Zhou S,Chang C

doi

10.1002/mp.13268

subject

Has Abstract

pub_date

2019-01-01 00:00:00

pages

215-228

issue

eissn

0094-2405

issn

2473-4209

journal_volume

pub_type

杂志文章

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Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.