Atten-Nonlocal Unet: Attention and Non-local Unet for medical image segmentation.
Authors
Affiliations (4)
Affiliations (4)
- School of Artificial Intelligence, Anhui University of Science and Technology, Huainan, 232001, China. Electronic address: [email protected].
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China. Electronic address: [email protected].
- Sleep Medicine Center in High-tech District Hospital and Department of Neurology, First Affiliated Hospital of Anhui University of Science and Technology, First People's Hospital of Huainan, Huainan, 232000, China. Electronic address: [email protected].
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China. Electronic address: [email protected].
Abstract
The convolutional neural network(CNN)-based models have emerged as the predominant approach for medical image segmentation due to their effective inductive bias. However, their limitation lies in the lack of long-range information. In this study, we propose the Atten-Nonlocal Unet model that integrates CNN and transformer to overcome this limitation and precisely capture global context in 2D features. Specifically, we utilize the BCSM attention module and the Cross Non-local module to enhance feature representation, thereby improving the segmentation accuracy. Experimental results on the Synapse, ACDC, and AVT datasets show that Atten-Nonlocal Unet achieves DSC scores of 84.15%, 91.57%, and 86.94% respectively, and has 95% HD of 15.17, 1.16, and 4.78 correspondingly. Compared to the existing methods for medical image segmentation, the proposed method demonstrates superior segmentation performance, ensuring high accuracy in segmenting large organs while improving segmentation for small organs.