  In this study, the performance of existing U-shaped neural network
architectures was enhanced for medical image segmentation by adding
Transformer. Although Transformer architectures are powerful at extracting
global information, its ability to capture local information is limited due to
its high complexity. To address this challenge, we proposed a new lightweight
U-shaped cascade fusion network (LUCF-Net) for medical image segmentation. It
utilized an asymmetrical structural design and incorporated both local and
global modules to enhance its capacity for local and global modeling.
Additionally, a multi-layer cascade fusion decoding network was designed to
further bolster the network's information fusion capabilities. Validation
results achieved on multi-organ datasets in CT format, cardiac segmentation
datasets in MRI format, and dermatology datasets in image format demonstrated
that the proposed model outperformed other state-of-the-art methods in handling
local-global information, achieving an improvement of 1.54% in Dice coefficient
and 2.6 mm in Hausdorff distance on multi-organ segmentation. Furthermore, as a
network that combines Convolutional Neural Network and Transformer
architectures, it achieves competitive segmentation performance with only 6.93
million parameters and 6.6 gigabytes of floating point operations, without the
need of pre-training. In summary, the proposed method demonstrated enhanced
performance while retaining a simpler model design compared to other
Transformer-based segmentation networks.
