UNet fusing patch attention for segmenting knee cartilage on MRI
10.13929/j.issn.1003-3289.2024.05.027
- VernacularTitle:融合分区注意力UNet模型用于分割MRI中的膝关节软骨
- Author:
Xiang WANG
1
;
Cao SHI
;
Zhengyi YUAN
Author Information
1. 青岛科技大学信息科学技术学院,山东青岛 266000
- Keywords:
knee joint;
cartilage;
deep learning;
magnetic resonance imaging;
attention mechanism
- From:
Chinese Journal of Medical Imaging Technology
2024;40(5):764-768
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a UNet fusing patch attention(PA-UNet)model,and to observe its value for segmenting knee cartilage on MRI.Methods Slice and preprocessing were performed on knee MRI selected from Osteoarthritis Initiative-Zuse Institute Berlin dataset.Taken UNet as the backbone network,a PA-UNet model was constructed based on patch attention mechanism.The effect of PA-UNet model and other models for segmenting both femoral cartilage and tibial cartilage were compared by subjective and objective evaluations.Ablation experiments based on UNet,UNet based on SE with layers 2-4(UNet+SE),+UNet,++UNet,+++UNet,+U-Net+,++U-Net++and PA-UNet models were performed to observe the effect of models for segmenting knee cartilage.Results PA-UNet could accurately segment femoral and tibial cartilage in all simple,medium and difficult samples,which had better segmenting effect on small structures than SegNet,UNet and DeepLabv3+models.The Dice similarity coefficient(DSC)and intersection over union of PA-UNet model for segmenting femoral and tibial cartilage were both higher,while Hausdorff distance of PA-UNet model was lower than those of UNet,DeepLabv3+,SA-UNet,RA UNet and SegNet models.DSC of PA-UNet model for segmenting femoral cartilage and tibial cartilage was 88.97％and 82.72％,respectively,both higher than those of UNet,UNet+SE,+UNet,++UNet,++UNet,+UNet+and++UNet++models.Conclusion PA-UNet could segment knee cartilage completely on MRI,especially for small structures.
