Contour extraction of medical images using an attention-based network

Abstract

A comprehensive analysis of medical images is important, as it assists in early screening and clinical treatment as well as subsequent rehabilitation. In general, the contour information can elaborately describe the shape and size of lesions in a medical image, which accurately reflects specific and valuable properties that facilitate the identification of abnormalities, so contour extraction is meaningful. However, the traditional method usually depends on the output of image segmentation, which causes blurred edges and loss of details. To address these issues, an effective attention-based network for contour extraction is proposed, where a model mixed with U-Net and an attention network is utilized to extract image features, and a multilayer perceptron (MLP) is employed to classify those features to obtain a clear contour. Compared with the existing methods, the experimental results on three datasets (Herlev, Drosophila, and ISIC-2017) show that the accuracy reaches approximately 93–98 % by using the proposed network, and the number of parameters is 46.4 % less than the deep active contour network (DACN). Such performances are impressive when considering accuracy and the number of parameters as the key concerns. Therefore, this study reduces the model computation with almost no loss of accuracy, which can satisfy clinical requirements for medical image analysis.