Chest X-ray Dataset with Lung Segmentation

Chest X-ray(CXR) images are prominent among medical images and are commonly administered in emergency diagnosis and treatment corresponding to cardiac and respiratory diseases. Though there are robust solutions available for medical diagnosis, validation of artificial intelligence (AI) in radiology is still questionable. Segmentation is pivotal in chest radiographs that aid in improvising the existing AI-based medical diagnosis process. We provide the CXLSeg dataset: Chest X-ray with Lung Segmentation, a comparatively large dataset of segmented Chest X-ray radiographs based on the MIMIC-CXR dataset, a popular CXR image dataset. The dataset contains segmentation results of 243,324 frontal view images of the MIMIC-CXR dataset and corresponding masks. Additionally, this dataset can be utilized for computer vision-related deep learning tasks such as medical image classification, semantic segmentation and medical report generation. Models using segmented images yield better results since only the features related to the important areas of the image are focused. Thus images of this dataset can be manipulated to any visual feature extraction process associated with the original MIMIC-CXR dataset and enhance the results of the published or novel investigations. Furthermore, masks provided by this dataset can be used to train segmentation models when combined with the MIMIC-CXR-JPG dataset. The SA-UNet model achieved a 96.80% in dice similarity coefficient and 91.97% in IoU for lung segmentation using CXLSeg.

胸片影像（CXR）在医学影像中占据显著地位，常用于心脏和呼吸系统疾病的急诊诊断与治疗。尽管在医学诊断方面已有稳健的解决方案，但人工智能（AI）在放射学领域的验证仍存疑。在胸片放射学中，分割技术至关重要，有助于优化现有的基于AI的医学诊断流程。本团队提供CXLSeg数据集：基于MIMIC-CXR数据集的肺部分割胸片，这是一个相对较大的分割胸片放射学数据集。该数据集包含MIMIC-CXR数据集中243,324张正面视图图像的分割结果及其相应的掩码。此外，此数据集还可用于计算机视觉相关的深度学习任务，如医学图像分类、语义分割和医学报告生成。使用分割图像的模型能够产生更优的结果，因为它们仅关注图像中的重要区域特征。因此，本数据集中的图像可以用于与原始MIMIC-CXR数据集相关的任何视觉特征提取过程，以增强已发表或新研究的成果。此外，结合MIMIC-CXR-JPG数据集，本数据集提供的掩码可用于训练分割模型。SA-UNet模型在使用CXLSeg进行肺部分割时，实现了96.80%的dice相似系数和91.97%的IoU。