[1] Bray F, Ferlay J, Soerjomataram I et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 68, 394-424(2018).

[2] Jiang N N, Zhang P, Liao L Z et al. Clinical value of CT and MRI in the diagnosis and differential diagnosis of renal cystic lesions[J]. Chinese and Foreign Medical Research, 19, 79-82(2021).

[3] Yang Q, Zhao Y Q, Zhang F et al. Image segmentation of liver CT sequence based on spatial fuzzy C-means and graph cut[J]. Laser & Optoelectronics Progress, 59, 1217002(2022).

[4] Ma W Y, Manjunath B S. EdgeFlow: a technique for boundary detection and image segmentation[J]. IEEE Transactions on Image Processing, 9, 1375-1388(2000).

[5] Batenburg K J, Sijbers J. Optimal threshold selection for tomogram segmentation by projection distance minimization[J]. IEEE Transactions on Medical Imaging, 28, 676-686(2009).

[6] Ugarriza L G, Saber E, Vantaram S R et al. Automatic image segmentation by dynamic region growth and multiresolution merging[J]. IEEE Transactions on Image Processing, 18, 2275-2288(2009).

[7] Sangewar S, Peshattiwar A A, Alagdeve V et al. Liver segmentation of CT scan images using K means algorithm[C], 6-9(2013).

[8] Zhang N, Su R, Lebonvallet S et al. Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence[C], 3373-3376(2010).

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

[10] 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).

[11] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C], 565-571(2016).

[12] Mu H W, Guo Y, Quan X H et al. Magnetic resonance imaging brain tumor image segmentation based on improved U-net[J]. Laser & Optoelectronics Progress, 58, 0410022(2021).

[13] Chu J H, Huang K L, Lü W. A method for brain tumor segmentation using cascaded modified U-net[J]. Laser & Optoelectronics Progress, 58, 0810020(2021).

[14] Zuo M, Liu Y Y, Cui H et al. Semantic segmentation method of point clouds based on sparse convolution and attention mechanism[J]. Laser & Optoelectronics Progress, 60, 2015002(2023).

[15] Shan C X, Li Q, Guan X. Lightweight brain tumor segmentation algorithm based on multi-view convolution[J]. Laser & Optoelectronics Progress, 60, 1010018(2023).

[16] Qin X B, Zhang Z C, Huang C Y et al. U2-Net: going deeper with nested U-structure for salient object detection[J]. Pattern Recognition, 106, 107404(2020).

[17] Vaswani A, Shazeer N, Parmar N et al. Attention is all You need[C], 6000-6010(2017).

[18] Wang L J, Lu H C, Ruan X et al. Deep networks for saliency detection via local estimation and global search[C], 3183-3192(2015).

[19] Zhang Y, Hua Q L, Xu D et al. A complex-valued convolutional neural network with different activation functions in polarimetric SAR image classification[C](2020).

[20] Zhao Z X, Chen K X, Yamane S. CBAM-Unet: easier to find the target with the attention module “CBAM”[C], 655-657(2021).

[21] Hendrycks D, Gimpel K. Gaussian error linear units (GELUs)[EB/OL]. https://arxiv.org/abs/1606.08415

[22] Sathianathen N, Heller N, Tejpaul R et al. Automatic segmentation of kidneys and kidney tumors: the KiTS19 international challenge[J]. Frontiers in Digital Health, 797607(2022).

[23] Hsiao C H, Lin P C, Chung L A et al. A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images[J]. Computer Methods and Programs in Biomedicine, 221, 106854(2022).

[24] Isensee F, Jaeger P F, Kohl S A A et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature Methods, 18, 203-211(2021).

[25] Kang L, Zhou Z Q, Huang J J et al. Renal tumors segmentation in abdomen CT Images using 3D-CNN and ConvLSTM[J]. Biomedical Signal Processing and Control, 72, 103334(2022).

[26] Guo J N, Zeng W, Yu S et al. RAU-net: U-net model based on residual and attention for kidney and kidney tumor segmentation[C], 353-356(2021).

[27] Zhao W S, Jiang D H, Queralta J P et al. MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net[J]. Informatics in Medicine Unlocked, 19, 100357(2020).

[28] Zhang Y, Wang Y X, Hou F et al. Cascaded volumetric convolutional network for kidney tumor segmentation from CT volumes[EB/OL]. https://arxiv.org/abs/1910.02235

[29] Hou X S, Xie C M, Li F Y et al. A triple-stage self-guided network for kidney tumor segmentation[C], 341-344(2020).