SCA-Net: A Spatial and Channel Attention Network for Medical Image Segmentation

• Published in: IEEE access Vol. 9; pp. 160926 - 160937
• Main Authors: Shan, Tong, Yan, Jiayong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; attention; Computer architecture; Context; Convolution; Decoding; Deep learning; Feature extraction; Image analysis; Image segmentation; Medical diagnostic imaging; medical image segmentation; Medical imaging; multiscale contextual information; Pancreas; Semantics; Task analysis; Thyroid gland
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3132293

Automatic medical image segmentation is a critical tool for medical image analysis and disease treatment. In recent years, convolutional neural networks (CNNs) have played an important role in this field, and U-Net is one of the most famous fully convolutional network architectures among many kinds of CNNs for medical segmentation tasks. However, the CNNs based on U-Net used for medical image segmentation rely only on simple concatenation operation of multiscale features. The spatial and channel context information is easily missed. To capture the spatial and channel context information and improve the segmentation performance, in this paper, a spatial and channel attention network (SCA-Net) is proposed. SCA-Net presents two novel blocks: a spatial attention block and a channel attention block. The spatial attention block (SAB) combines the multiscale information from high-level and low-level stages to learn more representative spatial features, and the channel attention block (CAB) redistributes the channel feature responses to strengthen the most critical channel information while restraining the irrelevant channels. Compared with other state-of-the-art networks, our proposed framework obtained better segmentation performance in each of the three public datasets. The average Dice score improved from 88.79% to 92.92% for skin lesion segmentation, 94.02% to 98.25% for thyroid gland segmentation and 87.98% to 91.37% for pancreas segmentation compared with U-Net. Additionally, the Bland-Altman analysis showed that our network had better agreement between automatic and manually calculated areas in each task.