TY - JOUR
T1 - CasUNeXt
T2 - A Cascaded Transformer With Intra- and Inter-Scale Information for Medical Image Segmentation
AU - Sun, Junding
AU - Zheng, Xiaopeng
AU - Wu, Xiaosheng
AU - Tang, Chaosheng
AU - Wang, Shuihua
AU - Zhang, Yudong
N1 - Publisher Copyright:
© 2024 The Author(s). International Journal of Imaging Systems and Technology published by Wiley Periodicals LLC.
PY - 2024/9
Y1 - 2024/9
N2 - Due to the Transformer's ability to capture long-range dependencies through Self-Attention, it has shown immense potential in medical image segmentation. However, it lacks the capability to model local relationships between pixels. Therefore, many previous approaches embedded the Transformer into the CNN encoder. However, current methods often fall short in modeling the relationships between multi-scale features, specifically the spatial correspondence between features at different scales. This limitation can result in the ineffective capture of scale differences for each object and the loss of features for small targets. Furthermore, due to the high complexity of the Transformer, it is challenging to integrate local and global information within the same scale effectively. To address these limitations, we propose a novel backbone network called CasUNeXt, which features three appealing design elements: (1) We use the idea of cascade to redesign the way CNN and Transformer are combined to enhance modeling the unique interrelationships between multi-scale information. (2) We design a Cascaded Scale-wise Transformer Module capable of cross-scale interactions. It not only strengthens feature extraction within a single scale but also models interactions between different scales. (3) We overhaul the multi-head Channel Attention mechanism to enable it to model context information in feature maps from multiple perspectives within the channel dimension. These design features collectively enable CasUNeXt to better integrate local and global information and capture relationships between multi-scale features, thereby improving the performance of medical image segmentation. Through experimental comparisons on various benchmark datasets, our CasUNeXt method exhibits outstanding performance in medical image segmentation tasks, surpassing the current state-of-the-art methods.
AB - Due to the Transformer's ability to capture long-range dependencies through Self-Attention, it has shown immense potential in medical image segmentation. However, it lacks the capability to model local relationships between pixels. Therefore, many previous approaches embedded the Transformer into the CNN encoder. However, current methods often fall short in modeling the relationships between multi-scale features, specifically the spatial correspondence between features at different scales. This limitation can result in the ineffective capture of scale differences for each object and the loss of features for small targets. Furthermore, due to the high complexity of the Transformer, it is challenging to integrate local and global information within the same scale effectively. To address these limitations, we propose a novel backbone network called CasUNeXt, which features three appealing design elements: (1) We use the idea of cascade to redesign the way CNN and Transformer are combined to enhance modeling the unique interrelationships between multi-scale information. (2) We design a Cascaded Scale-wise Transformer Module capable of cross-scale interactions. It not only strengthens feature extraction within a single scale but also models interactions between different scales. (3) We overhaul the multi-head Channel Attention mechanism to enable it to model context information in feature maps from multiple perspectives within the channel dimension. These design features collectively enable CasUNeXt to better integrate local and global information and capture relationships between multi-scale features, thereby improving the performance of medical image segmentation. Through experimental comparisons on various benchmark datasets, our CasUNeXt method exhibits outstanding performance in medical image segmentation tasks, surpassing the current state-of-the-art methods.
KW - cascade
KW - CNN
KW - multi-scale features
KW - transformer
UR - http://www.scopus.com/inward/record.url?scp=85204705531&partnerID=8YFLogxK
U2 - 10.1002/ima.23184
DO - 10.1002/ima.23184
M3 - Article
AN - SCOPUS:85204705531
SN - 0899-9457
VL - 34
JO - International Journal of Imaging Systems and Technology
JF - International Journal of Imaging Systems and Technology
IS - 5
M1 - e23184
ER -