Lung contour detection in Chest X-ray images using Mask Region-based Convolutional Neural Network and Adaptive Closed Polyline Searching Method

Detection of lung contour on chest X-ray images (CXRs) is a necessary step for computer-aid medical imaging analysis. Because of the low-intensity contrast around lung boundary and large inter-subject variance, it is challenging to detect lung from structural CXR images accurately. To tackle this problem, we design an automatic and hybrid detection network containing two stages for lung contour detection on CXRs. In the first stage, an image preprocessing stage based on a deep learning model is used to automatically extract coarse lung contours. In the second stage, a refinement step is used to fine-tune the coarse segmentation results based on an improved principal curve-based method coupled with an improved machine learning method. The model is evaluated on several public datasets, and experiments demonstrate that the performance of the proposed method outperforms state-of-the-art methods.Clinical Relevance - This can help radiologists for automatic separate lung, which can decrease the workloads of the radiologists' manually delineated lung contour in CXRs.