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.

搜集汇总

背景与挑战

背景概述

Chest X-ray Dataset with Lung Segmentation（CXLSeg）是一个基于MIMIC-CXR的较大规模胸部X光片分割数据集，包含243,324张正面视图图像及其对应的分割掩膜。该数据集适用于医学图像分类、语义分割和报告生成等深度学习任务，SA-UNet模型在肺部分割上达到了96.80%的Dice相似系数和91.97%的IoU，展示了其有效性。