Automatic segmentation method using FCN with multi-scale dilated convolution for medical ultrasound image

Image segmentation plays a critical role in the quantitative and qualitative analysis of medical ultrasound images, directly affecting the follow-up analysis and processing. However, due to the speckle noise, fuzziness, complexity and diversity of medical ultrasound images, the traditional image segmentation algorithms are accessible to leak the boundary at the weak edge of the medical ultrasound image, getting inaccurate results and difficulty extracting the target contour of the ultrasound image. In addition, the non-automatic feature extraction method cannot realize the end-to-end automatic segmentation function. Nevertheless, fully convolutional networks (FCNs) can realize end-to-end automatic semantic segmentation, and are widely used for ultrasound image segmentation. In this paper, we aim at the problems of low segmentation accuracy and long segmentation time in the traditional segmentation method, proposing a novel segmentation method based on an improved FCN with multi-scale dilated convolution for ultrasound image segmentation. The proposed method firstly preprocesses medical ultrasound images through image filtering, normalization and enhancement, and then improves the fully convolutional neural network by constructing four-dilated convolutions with different dilation rates, which can capture multi-scale context feature information and finally postprocesses the segmentation results of the medical ultrasound image by the Laplace correction operator. Our experiments demonstrate that the proposed method achieves better segmentation results than state-of-the-art methods on the breast ultrasound dataset.