[1] Moltz J H, Bornemann L, Kuhnigk J M et al. Advanced segmentation techniques for lung nodules, liver metastases, and enlarged lymph nodes in CT scans[J]. IEEE Journal of Selected Topics in Signal Processing, 3, 122-134(2009).

[2] Lu X Q, Wu J S, Ren X Y et al. The study and application of the improved region growing algorithm for liver segmentation[J]. Optik, 125, 2142-2147(2014).

[3] Casciaro S, Franchini R, Massoptier L et al. Fully automatic segmentations of liver and hepatic tumors from 3-D computed tomography abdominal images: comparative evaluation of two automatic methods[J]. IEEE Sensors Journal, 12, 464-473(2012).

[4] Li B N, Chui C K, Chang S et al. A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images[J]. Expert Systems with Applications, 39, 9661-9668(2012).

[5] Zareei A, Karimi A. Liver segmentation with new supervised method to create initial curve for active contour[J]. Computers in Biology and Medicine, 75, 139-150(2016).

[6] Shimizu A, Narihira T, Furukawa D et al[2021-02-25]. Ensemble segmentation using AdaBoost with application to liver lesion extraction from a CT volume [2021-02-25].https: ∥www.researchgate.net/publication/28359593_Ensemble_segmentation_using_AdaBoost_with_application_to_liver_lesion_extraction_from_a_CT_volume..

[7] Conze P H, Noblet V, Rousseau F et al. Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced CT scans[C]∥2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), April 13-16, 2016, Prague, Czech Republic., 416-419(2016).

[8] Vorontsov E, Abi-Jaoudeh N, Kadoury S. Metastatic liver tumor segmentation using texture-based omni-directional deformable surface models[M]. ∥Yoshida H, Näppi J J, Saini S. Abdominal imaging. Computational and clinical applications. Lecture notes in computer science. Cham: Springer, 8676, 74-83(2014).

[9] Ronneberger O, Fischer P. -05-18)[2021-02-25]. https: ∥arxiv., org/abs/1505, 04597(2015).

[10] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[11] Chen L C, Papandreou G, Schroff F et al. -06-17)[2021-02-25]. https: ∥arxiv., org/abs/1706, 05587(2017).

[12] [12] JégouS, DrozdzalM, VazquezD, et al.The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 1175- 1183.

[13] Christ P F. Elshaer M E A, Ettlinger F, et al. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields[M]. ∥Ourselin S, Joskowicz L, Sabuncu M R, et al. Medical image computing and computer-assisted intervention-MICCAI 2016. Lecture notes in computer science. Cham: Springer, 9901, 415-423(2016).

[14] Chlebus G, Meine H, Moltz J H et al. -06-02)[2021-02-25]. https: ∥arxiv., org/abs/1706, 00842(2017).

[16] Huang Y J, Shi Z F, Wang Z Q et al. Improved U-Net based on mixed loss function for liver medical image segmentation[J]. Laser & Optoelectronics Progress, 57, 221003(2020).

[17] Zhou Z W. Siddiquee M M R, Tajbakhsh N, et al. UNet++: a nested U-Net architecture for medical image segmentation[M]. ∥Stoyanov D, Taylor Z, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science. Cham: Springer, 11045, 3-11(2018).

[18] Li X M, Chen H, Qi X J et al. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes[J]. IEEE Transactions on Medical Imaging, 37, 2663-2674(2018).

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

[20] Ai L M, Li T D, Liao F Y et al. Magnetic resonance brain tumor image segmentation based on attention U-Net[J]. Laser & Optoelectronics Progress, 57, 141030(2020).

[21] Chen Y, Wang K, Liao X et al. Channel-UNet: a spatial channel-wise convolutional neural network for liver and tumors segmentation[J]. Frontiers in Genetics, 10, 1110(2019).

[22] 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, 40, 1710001(2020).

[23] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11211, 833-851(2018).

[24] Zheng T Y, Tang C, Lei Z K. Multi-scale retinal vessel segmentation based on fully convolutional neural network[J]. Acta Optica Sinica, 39, 0211002(2019).

[25] Wu C Y, Yi B S, Zhang Y G et al. Retinal vessel image segmentation based on improved convolutional neural network[J]. Acta Optica Sinica, 38, 1111004(2018).

[26] 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, 57, 141009(2020).

[27] He K M, Zhang X Y, Ren S 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., 770-778(2016).

[28] Huang G. Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).

[29] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).