[1] Selle D, Preim B, Schenk A et al. Analysis of vasculature for liver surgical planning[J]. IEEE Transactions on Medical Imaging, 21, 1344-1357(2002).

[2] Huang Q, Sun J F, Ding H et al. Robust liver vessel extraction using 3D U-Net with variant dice loss function[J]. Computers in Biology and Medicine, 101, 153-162(2018).

[3] Zhang H H, Bai P R, Min X L et al. Hepatic vessel segmentation based on animproved 3D region growing algorithm[J]. Journal of Physics: Conference Series, 1486, 032038(2020).

[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. Lecture notes in computer science, 9351, 234-241(2015).

[5] Yin X H, Wang Y C, Li D Y. Survey of medical image segmentation technology based on U-Net structure improvement[J]. Journal of Software, 32, 519-550(2021).

[6] Liang L M, Sheng X Q, Lan Z M et al. U-shaped retinal vessel segmentation algorithm based on adaptive scale information[J]. Acta Optica Sinica, 39, 0810004(2019).

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

[8] Li R, Li M, Li J et al. Connection sensitive attention U-NET for accurate retinal vessel segmentation[EB/OL]. https://arxiv.org/abs/1903.05558v2

[9] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[10] Pereira S, Pinto A, Amorim J et al. Adaptive feature recombination and recalibration for semantic segmentation with fully convolutional networks[J]. IEEE Transactions on Medical Imaging, 38, 2914-2925(2019).

[11] 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).

[12] Tetteh G, Efremov V, Forkert N D et al. DeepVesselNet: vessel segmentation, centerline prediction, and bifurcation detection in 3-D angiographic volumes[J]. Frontiers in Neuroscience, 14, 592352(2020).

[13] Salehi S S M, Erdogmus D, Gholipour A. Tversky loss function for image segmentation using 3D fully convolutional deep networks[M]. Wang Q, Shi Y H, Suk H I, et al. Machine learning in medical imaging. Lecture notes in computer science, 10541, 379-387(2017).

[14] Sudre C H, Li W Q, Vercauteren T et al. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations[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, 10553, 240-248(2017).

[15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[16] Xie S N, Girshick R, Dollár P et al. Aggregated residual transformations for deep neural networks[C], 5987-5995(2017).

[17] 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).

[18] Simpson A L, Antonelli M, Bakas S et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms[EB/OL]. https://arxiv.org/abs/1902.09063

[19] Perslev M, Dam E B, Pai A et al. One network to segment them all: a general, lightweight system for accurate 3D 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, 11765, 30-38(2019).

[20] Xia Y D, Liu F Z, Yang D et al. 3D semi-supervised learning with uncertainty-aware multi-view co-training[C], 3635-3644(2020).

[21] Isensee F, Petersen J, Kohl S A A et al. nnU-net: breaking the spell on successful medical image segmentation[EB/OL]. https://arxiv.org/abs/1904.08128v1

[22] Lee K, Sunwoo L, Kim T et al. Spider U-net: incorporating inter-slice connectivity using LSTM for 3D blood vessel segmentation[J]. Applied Sciences, 11, 2014(2021).

[23] Yu Q H, Yang D, Roth H et al. C2FNAS: coarse-to-fine neural architecture search for 3D medical image segmentation[C], 4125-4134(2020).