Automatic pneumothorax segmentation on chest X-ray images is very crucial for diagnosis and treatment as large pneumothorax could be fatal. The pneumothorax segmentation is challenging, as some small pneumothoraces can be subtle, and may overlap with the ribs and clavicles. Meanwhile, the shape variation of pneumothorax is also very large, which also makes the segmentation more difficult. In this paper, we propose a novel automated pneumothorax segmentation framework which consists of three modules: 1) a fully convolutional DenseNet (FC-DenseNet), 2) a spatial and channel squeeze and excitation module (scSE), and 3) a multi-scale module. In order to improve boundary segmentation accuracy, a novel spatial weighted cross-entropy loss function is proposed, which penalize the target, background and contour pixels with different weights. Extensive experiments are conducted on the 2213 chest X-ray images of testing data and the results suggest that proposed segmentation algorithm outperforms the state-of-the-art methods in terms of mean pixel-wise accuracy (MPA) of 0.93±0.13 and dice similarity coefficient (DSC) of 0.92±0.14 etc. Accordingly, the effectiveness of our method is corroborated.