EJ-TransUNet: fusion of convolutional and multiscale transUNet abdominal multiorgan segmentation networks

In computer-aided diagnosis, abdominal multi-organ segmentation is essential, and it has significant research implications. However, the ambiguous boundaries, complex backgrounds, and variable shapes and sizes of abdominal multi-organs make this task extremely challenging. To this goal, an abdominal multi-organ segmentation network called EJTransUNet—a fused convolutional and multi-scale TransUNet network—is proposed. Three components make up the network: a jump connection, a decoder, and an encoder. Regarding the input picture, firstly, a global local enhancement module (DCE) is designed in the hybrid encoder CNN-Transformer module, and the hollow convolution is introduced in the last layer of the Transformer module, which enhances the extraction of global local contextual information, so that the model strengthens the local correlation of the features while establishing the long-distance dependency; secondly, a jump connection stage is designed in the Multi-scale fusion module MSC, which utilizes the CBAM attention mechanism module to capture the correlation between features at different levels and focus on features at different spatial locations to improve the accuracy of target localization; and finally in the decoder output image. Using the Synapse dataset, the model's performance is assessed, yielding an average Dice Similarity Coefficient (DSC) of 80.34%. The experimental findings demonstrate that the suggested approach performs better overall than multiple comparison networks and yields superior segmentation outcomes for organs with varying sizes and shapes.