DeepSDM: Boundary-aware Pneumothorax Segmentation in Chest X-Ray Images

Abstract Accurate segmentation of the pneumothorax from chest X-ray images is of considerable significance for the diagnosis in the emergency department. Recently, deep learning methods have achieved impressive progress in many medical image segmentation tasks. However, the blurred boundary of pneumothorax in chest X-ray images makes it difficult for these algorithms to perform well in pneumothorax segmentation. In this paper, we aim to solve the problem in current learning-based segmentation methods that they often ignore the information of the target boundary and thus fail to generate the result with the correct shape. The proposed new learning framework, DeepSDM, employs the rich information in Signed Distance Map (SDM). The binary segmentation mask and SDM are learned in parallel via the multi-task strategy. Moreover, a boundary-based weighting scheme is adopted in the SDM regression task, forcing the model to focus more on pneumothorax and its contour. The proposed DeepSDM is validated by extensive experiments on the Kaggle SIIM-ACR Pneumothorax Segmentation dataset and our PTX-498 dataset. The results demonstrate that DeepSDM effectively perceives the boundary of pneumothorax and outperforms other state-of-the-art methods. To provoke the research interest in the community, the PTX-498 dataset, codes, and trained models are publicly available at Zenodo and GitHub.