[1] [1] AGHAMOHAMMADI A, RANJBARZADEH R, NAIEMI F, et al. TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach[J]. Expert systems with applications, 2021, 183: 115406.

[2] [2] SIRI S K, KUMAR S P, LATTE V M. Threshold-based new segmentation model to separate the liver from CT scan images[J]. IETE journal of research, 2022, 68(6): 4468-4475.

[3] [3] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//18th International Conference on Medical Image Computing and Computer-Assisted Intervention, October 5-9, 2015, Munich, Germany. Heidelberg: Springer International Publishing, 2015: 234-241.

[4] [4] ZHU F, CUI J, ZHU B, et al. Semantic segmentation of urban street scene images based on improved U-Net network[J]. Optoelectronics letters, 2023, 19(3): 179-185.

[5] [5] ZHOU Z, RAHMAN S M M, TAJBAKHSH N, et al. Unet++: a nested U-net architecture for medical image segmentation[C]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, September 20, 2018, Granada, Spain. Heidelberg: Springer International Publishing, 2018: 3-11.

[6] [6] VALANARASU J M J, PATEL V M. Unext: MLP-based rapid medical image segmentation network[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, October 8-12, 2022, Vancouver, BC, Canada. Cham: Springer Nature Switzerland, 2022: 23-33.

[7] [7] IEK , ABDULKADIR A, LIENKAMP S S, et al.3D U-Net: learning dense volumetric segmentation from sparse annotation[C]//19th International Conference on Medical Image Computing and Computer-Assisted Intervention, October 17-21, 2016, Athens, Greece. Heidelberg: Springer International Publishing, 2016: 424-432.

[8] [8] MILLETARI F, NAVAB N, AHMADI S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[EB/OL]. (2016-06-15)[2024-01-23]. https://arxiv.org/abs/1606.04797.

[9] [9] AHMED S F, RAHMAN F S, TABASSUM T, et al. 3D U-Net: fully convolutional neural network for automatic brain tumor segmentation[C]//22nd International Conference on Computer and Information Technology (ICCIT), December 18-20, 2019, Dhaka, Bangladesh. New York: IEEE, 2019: 1-6.

[10] [10] AFIFA K, AHMED M, KUN H, et al. 3D-DenseUNet: a new model for brain segmentation in magnetic resonance images[EB/OL]. (2023-12-06) [2024-01-23]. https://doi.org/10.21203/rs.3.rs-3710057/v1.

[11] [11] KEMASSI O, MAAMRI O, BOUANANE K, et al. Dilated convolutions based 3D U-net for multi-modal brain image segmentation[C]//International Conference on Artificial Intelligence and its Applications, January 24-26, 2021. Cham: Springer International Publishing, 2021: 428-436.

[12] [12] KE T, YANG X, ZHANG X, et al. VAC-UNet: visual attention convolution U-Net for 3D medical image segmentation[C]//IEEE International Conference on Medical Artificial Intelligence, November 18-19, 2023, Beijing, China. New York: IEEE, 2023: 435-440.

[13] [13] YANG J, MARCUS D S, SOTIRAS A. Dynamic U-net: adaptively calibrate features for abdominal multi-organ segmentation[EB/OL]. (2024-03-14) [2024-04-13]. https://arxiv.org/abs/2403.07303.

[14] [14] CHEN J, MEI J, LI X, et al. 3D TransUNet: advancing medical image segmentation through vision transformers[EB/OL]. (2024-03-14) [2024-04-13]. https://arxiv.org/abs/2310.07781.

[15] [15] ABOUSSALEH I, RIFFI J, EL FAZAZY K, et al.3DUV-NetR+: a 3D hybrid semantic architecture using transformers for brain tumor segmentation with multimodal MR images[J]. Results in engineering, 2024: 101892.

[16] [16] XIE E, WANG W, YU Z, et al. SegFormer: simple and efficient design for semantic segmentation with transformers[J]. Advances in neural information processing systems, 2021, 34: 12077-12090.

[17] [17] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision, September 8-14, 2018, Munich, Germany. Heidelberg: Springer International Publishing, 2018: 3-19.

[18] [18] SALEHI S S M, ERDOGMUS D, GHOLIPOUR A. Tversky loss function for image segmentation using 3D fully convolutional deep networks[C]//International Workshop on Machine Learning in Medical Imaging, September 10, 2017, Quebec City, QC, Canada. Cham: Springer International Publishing, 2017: 379-387.

[19] [19] XIAO X, HU Q V, WANG G. Edge-aware multi-task network for integrating quantification segmentation and uncertainty prediction of liver tumor on multi-modality non-contrast MRI[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, October 8-12, 2023, Vancouver, BC, Canada. Cham: Springer Nature Switzerland, 2023: 652-661.

[20] [20] CHEN Y, LIU L, LI J, et al. MetaLR: meta-tuning of learning rates for transfer learning in medical imaging[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, October 8-12, 2023, Vancouver, BC, Canada. Cham: Springer Nature Switzerland, 2023: 706-716.

[21] [21] YOU C, DAI W, MIN Y, et al. Implicit anatomical rendering for medical image segmentation with stochastic experts[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, October 8-12, 2023, Vancouver, BC, Canada. Cham: Springer Nature Switzerland, 2023: 561-571.

[22] [22] OH N, KIM J H, RHU J, et al. Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning[J]. Scientific reports, 2023, 13(1): 17605.

[23] [23] ANSARI M Y, YANG Y, MEHER P K, et al. Dense-PSP-UNet: a neural network for fast inference liver ultrasound segmentation[J]. Computers in biology and medicine, 2023, 153: 106478.