Feature‐guided attention network for medical image segmentation

Abstract

Abstract Background U‐Net and its variations have achieved remarkable performances in medical image segmentation. However, they have two limitations. First, the shallow layer feature of the encoder always contains background noise. Second, semantic gaps exist between the features of the encoder and the decoder. Skip‐connections directly connect the encoder to the decoder, which will lead to the fusion of semantically dissimilar feature maps. Purpose To overcome these two limitations, this paper proposes a novel medical image segmentation algorithm, called feature‐guided attention network, which consists of U‐Net, the cross‐level attention filtering module (CAFM), and the attention‐guided upsampling module (AUM). Methods In the proposed method, the AUM and the CAFM were introduced into the U‐Net, where the AUM learns to filter the background noise in the low‐level feature map of the encoder and the CAFM tries to eliminate the semantic gap between the encoder and the decoder. Specifically, the AUM adopts a top‐down pathway to use the high‐level feature map so as to filter the background noise in the low‐level feature map of the encoder. The AUM uses the encoder features to guide the upsampling of the corresponding decoder features, thus eliminating the semantic gap between them. Four medical image segmentation tasks, including coronary atherosclerotic plaque segmentation (Dataset A), retinal vessel segmentation (Dataset B), skin lesion segmentation (Dataset C), and multiclass retinal edema lesions segmentation (Dataset D), were used to validate the proposed method. Results For Dataset A, the proposed method achieved higher Intersection over Union (IoU) (), dice (), accuracy (), and sensitivity () than the previous best method: CA‐Net. For Dataset B, the proposed method achieved higher sensitivity (83.50%) and accuracy (97.55%) than the previous best method: SCS‐Net. For Dataset C, the proposed method had highest IoU () and dice () than those of all compared previous methods. For Dataset D, the proposed method had highest dice (average: 81.53%; retina edema area [REA]: 83.78%; pigment epithelial detachment [PED] 77.13%), sensitivity (REA: 89.01%; SRF: 85.50%), specificity (REA: 99.35%; PED: 100.00), and accuracy (98.73%) among all compared previous networks. In addition, the number of parameters of the proposed method was 2.43 M, which is less than CA‐Net (3.21 M) and CPF‐Net (3.07 M). Conclusions The proposed method demonstrated state‐of‐the‐art performance, outperforming other top‐notch medical image segmentation algorithms. The CAFM filtered the background noise in the low‐level feature map of the encoder, while the AUM eliminated the semantic gap between the encoder and the decoder. Furthermore, the proposed method was of high computational efficiency.