Chest X-ray segmentation images based on MIMIC-CXR

As more and more artificial intelligence (AI) or deep learning technologies have been applied to medical image applications such as radiological finding identification in chest X-rays (CXRs), the interpretability of the prediction model is crucial for building trust in AI. In pulmonary pathology detection, the CXR images with proper anatomical segmentations could aid in interpreting the models. However, the accuracy of the auto-segmentation algorithms was not high enough to create such a benchmark. In this project, we provided segmentation results of 1,141 frontal-view CXRs randomly selected from the MIMIC-CXR database. These CXRs were first processed into a pair of segmented images with the lung lobes and the rest parts by deep learning-based algorithms. We then manually filtered out the incorrect segmentation results. The segmented images maybe helpful for model interpretability.

随着人工智能（AI）或深度学习技术在医学影像应用，如胸部X光片（CXRs）的放射学发现识别等领域的广泛应用，预测模型的可解释性对于建立对AI的信任至关重要。在肺病理检测中，具有适当解剖分割的CXR图像有助于解释模型。然而，自动分割算法的准确性尚不足以创建此类基准。在本项目中，我们提供了从MIMIC-CXR数据库中随机选取的1,141张正面视图CXRs的分割结果。这些CXRs首先通过基于深度学习的算法进行处理，形成一对分割图像，包括肺叶和其他部分。然后，我们手动筛选出了错误的分割结果。这些分割图像可能有助于提高模型的可解释性。