Contour-aware semantic segmentation network with spatial attention mechanism for medical image

Abstract

Medical image segmentation is a critical and important step for developing computer-aided system in clinical situations. It remains a complicated and challenging task due to the large variety of imaging modalities and different cases. Recently, Unet has become one of the most popular deep learning frameworks because of its accurate performance in biomedical image segmentation. In this paper, we propose a contour-aware semantic segmentation network, which is an extension of Unet, for medical image segmentation. The proposed method includes a semantic branch and a detail branch. The semantic branch focuses on extracting the semantic features from shallow and deep layers; the detail branch is used to enhance the contour information implied in the shallow layers. In order to improve the representation capability of the network, a MulBlock module is designed to extract semantic information with different receptive fields. Spatial attention module (CAM) is used to adaptively suppress the redundant features. In comparison with the state-of-the-art methods, our method achieves a remarkable performance on several public medical image segmentation challenges.

Keywords: Medical image segmentation; Neural network; Semantic segmentation.

Fig. 1

Examples of several representative medical images. The first row indicates the lung nodule in CT images, the second row represents the skin lesion in the dermoscopy image, and the last row shows the colorectal polyp in endoscopy images

Fig. 2

Flowchart of the proposed method. The structure includes: (1) a detail branch with wide channels and shallow layers, used to capture the details of the underlying layer and generate high-resolution feature representations; (2) a semantic branch with narrow channels and deep layers used to get high-level semantic context information

Fig. 3

MultiBlock module

Fig. 4

Spatial attentive module. The size of each feature map is shown in $H\times W\times C$, where H, W, C indicate height, width and number of channels, respectively

Fig. 5

Visual comparisons to different methods on COVID-19 dataset. Red = TP, blue = TN, yellow = FN, and green = FP

Fig. 6

Visual comparisons to different methods on CVC-ClinicDB dataset. Red = TP, blue = TN, yellow = FN, and green = FP

Fig. 7

Visual comparisons to different methods on ISIC 2018 dataset. Red = TP, blue = TN, yellow = FN, and green = FP

Fig. 8

Visual comparisons to different methods on Lung segmentation dataset. Red = TP, blue = TN, yellow = FN, and green = FP