MLPSeg: Incorporating Multi-Local Perception with Context Cross Attention Based Transformer for Nuclei Segmentation

Nuclei detection and segmentation are indispensable prerequisites in digital pathology research, whereas the precise segmentation of nuclei by domain experts relies heavily on global spatial information and the inter-nuclei correlation. However, previous automatic nuclei segmentation works are mostly built on convolutional neural networks, which are unable to capture long-range global context with their inherent convolutional operations. Additionally, window-based design in few transformer-based approaches limits remote token interactions. The present study introduces a novel Multi-Local Perception (MLP) network, MLPSeg, which is proposed to address the aforementioned challenging issues. Specifically, the parallel computation of depthwise separable convolution and local window attention is designed to extract local information. Then, the parallel module of local horizontal attention and local vertical attention is designed to establish the global dependency. Moreover, to model the cross-scale dependencies and narrow the contextual semantic gap, the Context Cross Attention (CCA) is introduced for optimising skip connections. A tri-decoder structure is adopted to generate nuclei instance masks, normal edge masks and clustered edge masks. The superior performance of MLPSeg for nuclei segmentation is demonstrated across two datasets with different modalities, resulting in a 2.29% - 5.82% improvement compared to the state-of-the-art methods.