[1] Armato S G, Giger M L, MacMahon H. Automated lung segmentation in digitized posteroanterior chest radiographs[J]. Academic Radiology, 1998, 5(4): 245-255.

[2] van Ginneken B, Stegmann M B, Loog M. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database[J]. Medical Image Analysis, 2006, 10(1): 19-40.

[3] Guo S W, Fei B W. A minimal path searching approach for active shape model (ASM)-based segmentation of the lung[J]. Proceedings of SPIE, 2009, 7259: 72594B.

[4] 罗海峰, 翟荣存. 基于小波变换与Snake模型的肺野图像分割方法[J]. 计算机应用与软件, 2013, 30(11): 176-179.

    Luo H F, Zhai R C. A lung field image segmentation method based on wavelet analysis and Snake model[J]. Computer Applications and Software, 2013, 30(11): 176-179.

[5] 阮宏洋, 陈志澜, 程英升, 等. C-3D可变形卷积神经网络模型的肺结节检测[J]. 激光与光电子学进展, 2020, 57(4): 041013.

    Ruan H Y, Chen Z L, Cheng Y S, et al. Detection of pulmonary nodules based on C-3D deformable convolutional neural network model[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041013.

[6] 李大湘, 张振. 基于改进U-Net视网膜血管图像分割算法[J]. 光学学报, 2020, 40(10): 1010001.

    Li D X, Zhang Z. Improved U-Net segmentation algorithm for the retinal blood vessel images[J]. Acta Optica Sinica, 2020, 40(10): 1010001.

[7] 冯博文, 吕晓琪, 谷宇, 等. 基于空洞卷积的三维并行卷积神经网络脑肿瘤分割[J]. 激光与光电子学进展, 2020, 57(14): 141009.

    Feng B W, Lü X Q, Gu Y, et al. Three-dimensional parallel convolution neural network brain tumor segmentation based on dilated convolution[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141009.

[8] 张文秀, 朱振才, 张永合, 等. 基于残差块和注意力机制的细胞图像分割方法[J]. 光学学报, 2020, 40(17): 1710001.

    Zhang W X, Zhu Z C, Zhang Y H, et al. Cell image segmentation method based on residual block and attention mechanism[J]. Acta Optica Sinica, 2020, 40(17): 1710001.

[9] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.

[10] RonnebergerO, FischerP, BroxT. U-Net: convolutional networks for biomedical image segmentation[M]∥Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science. Cham: Springer, 2015, 9351: 234-241. 10.1007/978-3-319-24574-4_28

[11] SzegedyC, LiuW, JiaY Q, et al. Going deeper with convolutions[C]‍∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE Press, 2015: 1-9. 10.1109/cvpr.2015.7298594

[12] HeK M, ZhangX Y, RenS Q, et al. Deep residual learning for image recognition[C]‍∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE Press, 2016: 770-778. 10.1109/cvpr.2016.90

[13] HuangG, LiuZ, van der MaatenL, et al. Densely connected convolutional networks[C]‍∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 2261-2269. 10.1109/cvpr.2017.243

[14] RoyA G, NavabN, WachingerC. Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks[M]∥Frangi A F, Schnabel J A, Davatzikos C, et al. Medical image computing and computer assisted intervention-MICCAI 2018. Lecture notes in computer science. Cham: Springer, 2018, 11070: 421-429. 10.1007/978-3-030-00928-1_48

[15] 佘广南, 陈莹胤, 钟丽明, 等. 基于密集特征匹配的胸片肺野自动分割[J]. 南方医科大学学报, 2016, 36(1): 61-66.

    She G N, Chen Y Y, Zhong L M, et al. Automatic segmentation of lung fields in chest radiographs based on dense matching of local features[J]. Journal of Southern Medical University, 2016, 36(1): 61-66.

[16] Mansoor A, Cerrolaza J J, Perez G, et al. A generic approach to lung field segmentation from chest radiographs using deep space and shape learning[J]. IEEE Transactions on Biomedical Engineering, 2020, 67(4): 1206-1220.

[17] HwangS, ParkS. Accurate lung segmentation via network-wise training of convolutional networks[M]∥Cardoso M J, Arbel T, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science. Cham: Springer, 2017, 10553: 92-99. 10.1007/978-3-319-67558-9_11

[18] 秦子亮, 李朝锋. 基于卷积神经网络的胸片肺野自动分割[J]. 传感器与微系统, 2017, 36(10): 64-66, 69.

    Qin Z L, Li C F. Automatic segmentation of lung fields in chest radiographs based on CNN[J]. Transducer and Microsystem Technologies, 2017, 36(10): 64-66, 69.

[19] Arbabshirani M R, Dallal A H, Agarwal C, et al. Accurate segmentation of lung fields on chest radiographs using deep convolutional networks[J]. Proceedings of SPIE, 2017, 10133: 1013305.

[20] KalinovskyA, KovalevV. Lung image segmentation using deep learning methods and convolutional neural networks[C]∥XIII International Conference on Pattern Recognition and Information Processing (PRIP-2016), October 3-5, 2016, Minsk, Belarus. [S.l.: s.n.], 2016: 21-24. 10.1007/978-3-319-54220-1_15

[21] ZhangZ J, FuH Z, DaiH, et al. ET-Net: a generic edge-aTtention guidance network for medical image segmentation[M]∥Shen D G, Liu T M, Peters T M, et al. Medical image computing and computer assisted intervention-MICCAI 2019. Lecture notes in computer science. Cham: Springer, 2019, 11764: 442-450. 10.1007/978-3-030-32239-7_49

[22] PaszkeA, ChaurasiaA, KimS, et al. ENet: a deep neural network architecture for real-time semantic segmentation[EB/OL]. (2016-06-07)[2020-12-25]. https://arxiv.org/abs/1606.02147. 10.1109/icsip49896.2020.9339426

[23] Gu Z W, Cheng J, Fu H Z, et al. CE-Net: context encoder network for 2D medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2019, 38(10): 2281-2292.