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.

胸部X线（Chest X-ray, CXR）影像是医学影像领域的重要分支，广泛应用于心脏与呼吸系统疾病的急诊诊疗工作中。尽管目前已有成熟的医学诊断解决方案，但人工智能（Artificial Intelligence, AI）在放射学领域的有效性验证仍存在争议。胸部X光影像的分割是核心环节，可助力优化现有基于人工智能的医学诊断流程。本研究发布CXLSeg数据集——带肺部分割标注的胸部X线影像数据集，该数据集基于广受关注的CXR影像数据集MIMIC-CXR构建，属于规模较大的已标注胸部X线影像分割数据集。该数据集包含MIMIC-CXR数据集中243324张正位胸部X线影像的分割结果及其对应的掩码文件。此外，该数据集可用于计算机视觉相关的深度学习任务，例如医学影像分类、语义分割以及医学报告生成等。使用经分割处理的影像构建模型可获得更优性能，因为此类模型仅聚焦于影像关键区域的相关特征。因此，本数据集的影像可适配原始MIMIC-CXR数据集相关的任意视觉特征提取流程，从而提升已发表或全新研究的实验结果。进一步而言，将本数据集提供的掩码文件与MIMIC-CXR-JPG数据集结合使用，可用于训练分割模型。使用CXLSeg数据集开展肺部分割任务时，SA-UNet模型的骰子相似系数（Dice Similarity Coefficient）可达96.80%，交并比（Intersection over Union, IoU）可达91.97%。

搜集汇总

背景与挑战

背景概述

该数据集是基于MIMIC-CXR数据集构建的大规模胸部X光肺部分割数据集，包含243,324张正面视图的分割图像及对应掩码，是目前最大的公开分割胸部X光数据集。数据集使用SA-UNet模型生成高质量分割结果（Dice系数96.80%），并提供了完整的元数据、分割标签和预定义的数据划分，专门设计用于提升医学图像分析、肺部分割和医疗报告生成等深度学习任务的性能。