Fully Convolutional MultiScale ScSEDenseNet for Automatic Pneumothorax Segmentation in Chest Radiographs Automatic pneumothorax segmentation on chest Xray 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 (FCDenseNet), 2) a spatial and channel squeeze and excitation module (scSE), and 3) a multiscale module. In order to improve boundary segmentation accuracy, a novel spatial weighted crossentropy loss function is proposed, which penalize the target, background and contour pixels with different weights. Extensive experiments are conducted on the 2213 chest Xray images of testing data and the results suggest that proposed segmentation algorithm outperforms the stateoftheart methods in terms of mean pixelwise accuracy (MPA) of  0.93pm 0.13  and dice similarity coefficient (DSC) of  0.92pm 0.14  etc. Accordingly, the effectiveness of our method is corroborated.

Contouraware multilabel chest Xray organ segmentation Segmentation of organs from chest Xray images is an essential task for an accurate and reliable diagnosis of lung diseases and chest organ morphometry. In this study, we investigated the benefits of augmenting stateoftheart deep convolutional neural networks (CNNs) for image segmentation with organ contour information and evaluated the performance of such augmentation on segmentation of lung fields, heart, and clavicles from chest Xray images. Three stateoftheart CNNs were augmented, namely the UNet and LinkNet architecture with the ResNeXt feature extraction backbone, and the Tiramisu architecture with the DenseNet. All CNN architectures were trained on groundtruth segmentation masks and additionally on the corresponding contours. The contribution of such contourbased augmentation was evaluated against the contourfree architectures, and 20 existing algorithms for lung field segmentation. The proposed contouraware segmentation improved the segmentation performance, and when compared against existing algorithms on the same publicly available database of 247 chest Xray images, the UNet architecture with the ResNeXt50 encoder combined with the contouraware approach resulted in the best overall segmentation performance, achieving a Jaccard overlap coefficient of 0.971, 0.933, and 0.903 for the lung fields, heart, and clavicles, respectively. In this study, we proposed to augment CNN architectures for CXR segmentation with organ contour information and were able to significantly improve segmentation accuracy and outperform all existing solution using a public chest Xray database.