[1] Liu W, Jia F C, Hu Q M et al. Atlas based automatic liver 3D CT image segmentation[J]. Beijing Biomedical Engineering, 30, 457-461(2011).

[2] Jia T, Wei Y, Wu C D. 3D lung vessel image segmentation scheme based on geometric active contour model[J]. Chinese Journal of Scientific Instrument, 31, 2296-2301(2010).

[3] Li R Z, Liu Y Y, Yang M et al. Three-dimensional point cloud segmentation algorithm based on improved region growing[J]. Laser & Optoelectronics Progress, 55, 051502(2018).

[4] Ronneberger O, Fischer P, Brox T. 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, 9351, 234-241(2015).

[5] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 3431-3440(2015).

[6] Kaluva K C, Khened M, Kori A et al. 2D-densely connected convolution neural networks for automatic liver and tumor segmentation[EB/OL]. (2018-01-05)[2020-09-08], 02182. https://arxiv.org/abs/1802.02182

[7] Çiçek Ö, Abdulkadir A, Lienkamp S S et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation[C]. //Ourselin S, Joskowicz L, Sabuncu M R, et al. Medical image computing and computer-assisted intervention-MICCAI 2016, 9901, 424-432(2016).

[8] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C]. //2016 Fourth International Conference on 3D Vision (3DV), October 25-28, 2016, Stanford, CA, USA, 565-571(2016).

[9] He C E, Xu H J, Wang Z et al. Automatic segmentation algorithm for multimodal magnetic resonance-based brain tumor images[J]. Acta Optica Sinica, 40, 0610001(2020).

[10] 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).

[11] Christ P F, Ettlinger F, Grün F et al. Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks[EB/OL]. (2017-02-20)[2020-09-08]. https://arxiv.org/abs/1702.05970

[12] Oktay O, Schlemper J, Folgoc L L et al. Attention U-Net: learning where to look for the pancreas[EB/OL]. (2018-05-11)[2020-09-08]. https://arxiv.org/abs/1804.03999

[13] Lee C Y, Xie S N, Gallagher P et al. Deeply-supervised nets[EB/OL]. (2014-09-18)[2020-09-08]. https://arxiv.org/abs/1409.5185v2

[14] Zhou Z, Siddiquee M M R, Tajbakhsh N et al. UNet++: a nested U-Net architecture for medical image segmentation[J]. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 11045, 3-11(2018).

[15] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[EB/OL]. (2015-11-23)[2020-09-08]. https://arxiv.org/abs/1511.07122

[16] 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).

[17] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[18] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016, 9905, 21-37(2016).

[19] Liu S T, Huang D, Wang Y H. Receptive field block net for accurate and fast object detection[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11215, 404-419(2018).

[20] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

[21] Liu K W, Ma Y, Xiong H X et al. Medical-image super-resolution reconstruction method based on residual channel attention network[J]. Laser & Optoelectronics Progress, 57, 021014(2020).

[22] Jin Q, Meng Z, Sun C et al. RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans[J]. Frontiers in Bioengineering and Biotechnology, 8, 605132(2020).

[23] Chen X Y, Zhang R, Yan P K. Feature fusion encoder decoder network for automatic liver lesion segmentation[C]. //2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), April 8-11, 2019, Venice, Italy., 430-433(2019).

[24] Jiang H Y, Shi T, Bai Z Q et al. AHCNet: an application of attention mechanism and hybrid connection for liver tumor segmentation in CT volumes[J]. IEEE Access, 7, 24898-24909(2019).