摘要：Computer-Aided Diagnosis (CAD) has become a requisite and fundamental part in medical detection and diagnosis. As an indispensable component of CAD, the lung fields segmentation is critical for further analysis. Nowadays, it is well known that the methods of deep convolutional neural networks (DCNNs) have achieved outstanding performance in medical image segmentation. Especially the U-Net and its extensions have obtained promising accuracy on medical image segmentation. However, due to the superimposed regions in lung fields and varied shapes among different individuals, it is difficult to detect and segment the boundaries precisely. Besides, insufficient training dataset may results in poor generalization ability of the networks. To address these problems, this paper uses the standard U-Net as its backbone, and optimizes and improves it effectively. The proposed method can segment the lung fields in chest X-ray images automatically, which integrates U-Net, Bi-directional ConvLSTM (BConvLSTM), Squeeze and Excitation (SE), and fully connected CRF into one framework. In the proposed architecture, a single U-Net network is employed as the backbone. Then, the BConvLSTM is employed to concatenate the features extracted from the encoder and the corresponding decoder. In this way, the skip connection of the U-Net structure is replaced by BConvLSTM. Furthermore, the SE modules are embedded in the decoder to recalibrate the channel-wise features. Besides, the fully connected CRF is used to further refine the initial segmentation contours, which can fully consider the mutual information between pixels in the original image. Compared with diverse lung fields segmentation algorithms on JSRT and MC datasets, the superiority, effectiveness and robustness of the proposed method are verified.
