LA-TransUNet: Intracranial Hemorrhage CT Image Segmentation Network Based on Attention Mechanism

Intracranial hemorrhage(ICH) is a serious disease with high morbidity, recurrence, and disability rates, and computed tomography (CT) is considered an important standard for diagnosis. Since CT images of ICH have problems such as small bleeding areas, blurred boundaries, irregular shapes, and high noise, segmentation using separate networks such as U-Net or TransUNet often fails to obtain accurate results. To solve this problem, we propose an ICH CT image segmentation model (LA-TransUNet) that combines the attention mechanism (SEpro), large kernel convolution selection module (LSK-Block), Transformer, and U-Net. The network mainly contains three core components: encoder, decoder, and skip connection. The encoder introduced SEpro to enhance feature extraction capabilities and reduce the impact of noise interference. SEpro is introduced in the skip connection to improve the model's ability to capture edge detail information. The decoder introduced LSK-Block to focus on important features to enhance the model's perception of bleeding areas. Experimental results show that LA-TransUNet is superior to other mainstream segmentation networks regarding segmentation results and evaluation indicators.