DENSE SWIN-UNET: DENSE SWIN TRANSFORMERS FOR SEMANTIC SEGMENTATION OF PNEUMOTHORAX IN CT IMAGES

Abstract

Pneumothorax is a common yet potentially serious lung disease, which makes prompt diagnosis and treatment critical in clinical practice. Deep learning methods have proven effective in detecting pneumothorax lesions in medical images and providing quantitative analysis. However, due to the irregular shapes and uncertain positions of pneumothorax lesions, current segmentation methods must be further improved to increase accuracy. This study aimed to propose a Dense Swin-Unet algorithm that integrated the Dense Swin Transformer Block with the Swin-Unet model. The Dense Swin-Unet algorithm employed a sliding window self-attentiveness mechanism on different scales to enhance multiscale long-range dependencies. We designed an enhanced loss function that accelerated the convergence speed to address the issue of class imbalance. Given the limited availability of data in pneumothorax image processing, we created a new dataset and evaluated the efficacy of our model on this dataset. The results demonstrated that our lesion segmentation algorithm attained a Dice coefficient of 88.8%, representing a 1.5% improvement compared with previous deep learning algorithms. Notably, our algorithm achieved a significant enhancement in segmenting small microlesions.