Multi-Scale TransUnet Combined with CBAM for Nuclear Image Segmentation

The nucleus carries a wealth of genetic information that controls and regulates the characteristics and functions of cells. Its morphology and distribution are highly crucial for the differentiation and grading of tumors. However, with the widespread adoption of medical imaging technology, the task of handling massive cell images has become challenging and time-consuming, demanding specialized knowledge and expertise from medical professionals. The traditional nuclear image segmentation methods require expensive time consuming and exhibit limited adaptability to different environments. To address these problems, we propose a novel multi-scale TransUnet combined with Convolutional Block Attention Module (CBAM) method for nuclear image segmentation. Firstly, in the downsampling phase of the encoder, we introduce dilated convolution units with varying dilation rates. This enables the model to integrate multi-scale feature information, thereby expanding the receptive field during downsampling to extract cell nucleus information more comprehensively. In addition, to enhance the ability of target feature extraction, CBAM is added to the up-sampling of the decoder to sequentially acquire inter-channel dependencies and spatial pixel-level relationships. The experimental results show that the Dice coefficient of our model on the MoNuSeg dataset reaches 86.3%, and the crossover ratio of IoU reaches 84.2%. Compared with the second-best models (TransUnet), the performance of our model is improved by 1.22% and 0.45% in terms of IoU and Dice similarity coefficient, respectively.