  The UNet architecture, based on Convolutional Neural Networks (CNN), has
demonstrated its remarkable performance in medical image analysis. However, it
faces challenges in capturing long-range dependencies due to the limited
receptive fields and inherent bias of convolutional operations. Recently,
numerous transformer-based techniques have been incorporated into the UNet
architecture to overcome this limitation by effectively capturing global
feature correlations. However, the integration of the Transformer modules may
result in the loss of local contextual information during the global feature
fusion process. To overcome these challenges, we propose a 2D medical image
segmentation model called Multi-scale Cross Perceptron Attention Network
(MCPA). The MCPA consists of three main components: an encoder, a decoder, and
a Cross Perceptron. The Cross Perceptron first captures the local correlations
using multiple Multi-scale Cross Perceptron modules, facilitating the fusion of
features across scales. The resulting multi-scale feature vectors are then
spatially unfolded, concatenated, and fed through a Global Perceptron module to
model global dependencies. Furthermore, we introduce a Progressive Dual-branch
Structure to address the semantic segmentation of the image involving finer
tissue structures. This structure gradually shifts the segmentation focus of
MCPA network training from large-scale structural features to more
sophisticated pixel-level features. We evaluate our proposed MCPA model on
several publicly available medical image datasets from different tasks and
devices, including the open large-scale dataset of CT (Synapse), MRI (ACDC),
fundus camera (DRIVE, CHASE_DB1, HRF), and OCTA (ROSE). The experimental
results show that our MCPA model achieves state-of-the-art performance. The
code is available at
https://github.com/simonustc/MCPA-for-2D-Medical-Image-Segmentation.
