XLSor: A Robust and Accurate Lung Segmentor on Chest XRays Using CrissCross Attention and Customized Radiorealistic Abnormalities Generation This paper proposes a novel framework for lung segmentation in chest Xrays. It consists of two key contributions, a crisscross attention based segmentation network and radiorealistic chest Xray image synthesis (i.e. a synthesized radiograph that appears anatomically realistic) for data augmentation. The crisscross attention modules capture rich global contextual information in both horizontal and vertical directions for all the pixels thus facilitating accurate lung segmentation. To reduce the manual annotation burden and to train a robust lung segmentor that can be adapted to pathological lungs with hazy lung boundaries, an imagetoimage translation module is employed to synthesize radiorealistic abnormal CXRs from the source of normal ones for data augmentation. The lung masks of synthetic abnormal CXRs are propagated from the segmentation results of their normal counterparts, and then serve as pseudo masks for robust segmentor training. In addition, we annotate 100 CXRs with lung masks on a more challenging NIH Chest Xray dataset containing both posterioranterior and anteroposterior views for evaluation. Extensive experiments validate the robustness and effectiveness of the proposed framework. The code and data can be found from this https URL .

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.