CE-TransUnet: A Convolutional Enhanced Model for Pulmonary Alveolus Pathology Image Segmentation

Pulmonary alveolus segmentation plays an important role in the diagnosis of alveolar emphysema and lobar pneumonia. Besides, if segmented and calculated precisely, the area of tumor beds in non-small-cell lung cancers could be readily calculated and hence can help determine the severity of one's cancer. These factors render the segmentation of alveolar pathological images highly meaningful. However, we have not identified any existing publicly available dataset of alveolar pathological images and no existing methods focused on the segmentation of alveolus. Therefore, we are here to introduce our original Pulmonary Alveolus Pathology Image dataset PAPI . Additionally, those widely-used and several state-of-the-art medical segmentation methods perform passable, not expected, on PAPI. So we innovate our method Convolutional Enhanced Transformer-based U-net abbreviated as CE-TransUnet , which is a combination of improved U-net structure and our innovative CE-Transformer block. We circumspectly detect salient characteristics of the pulmonary alveolus and make counterpart improvements in both CE-Transformer blocks and U-net structure. Our experimental results have shown that these adjustments has made our model surpass the current common segmentation models in performance on PAPI and reach a Dice score of 95.31. We are also exploring the robustness of our model to adapt it to a wider range of scenarios.