A dual attention-guided transformer for multitissues segmentation of knee joint

Osteoarthritis is a leading cause of chronic disabilities. Automatic multi-tissues segmentation of knee joint can help doctors diagnose Osteoarthritis by segmenting knee joint MRI. However, manually segmenting is time-consuming for an experienced expert. Besides, this task is challenging due to the significant morphological differences and close proximity between various tissues in the knee joint. To achieve fast and accurate segmentation, we propose a novel hybrid architecture named nnCSCFormer (Not-another Cross-shaped Channel Transformer). This architecture integrates spatial and channel attention mechanisms to enable rapid and accurate automatic multi-tissues segmentation. By capturing the relationship between spatial and channel dimensions across the entire feature space, our model effectively extracts multitissues’specific information. Additionally, we introduce skip attention to aid the decoder in better preserving original image details. Experimental results demonstrate the efficacy of our model in simultaneously segmenting six tissues: femur bone, femoral cartilage, tibia bone, tibial cartilage, lateral meniscus and medial meniscus. The proposed method achieves superior segmentation performance compared to alternative methods on low-resolution knee MRI and has significant application value in preoperative planning for surgical navigation.