[1] Krishnapriya S, Karuna Y. A survey of deep learning for MRI brain tumor segmentation methods: trends, challenges, and future directions[J]. Health and Technology, 13, 181-201(2023).

[2] Yang J, Wang Y Y, Ai D N et al. Developments of multimodal image-guided surgical navigation[J]. Acta Optica Sinica, 43, 1500002(2023).

[3] Balwant M K. A review on convolutional neural networks for brain tumor segmentation: methods, datasets, libraries, and future directions[J]. IRBM, 43, 521-537(2022).

[4] Ilhan U, Ilhan A. Brain tumor segmentation based on a new threshold approach[J]. Procedia Computer Science, 120, 580-587(2017).

[5] Javadpour A, Mohammadi A. Improving brain magnetic resonance image (MRI) segmentation via a novel algorithm based on genetic and regional growth[J]. Journal of Biomedical Physics & Engineering, 6, 95-108(2016).

[6] Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods[J]. Procedia Computer Science, 102, 317-324(2016).

[7] Srinivasa Reddy A, Chenna Reddy P. MRI brain tumor segmentation and prediction using modified region growing and adaptive SVM[J]. Soft Computing, 25, 4135-4148(2021).

[8] Gu Y, Lu X Q, Zhang B H et al. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography[J]. PLoS One, 14, e0210551(2019).

[9] Thayumanavan M, Ramasamy A. Retracted: an efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier[J]. Concurrent Engineering, 29, 266-274(2021).

[10] Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019[J]. Pattern Recognition Letters, 131, 244-260(2020).

[11] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[12] Gu Y, Chi J Q, Liu J Q et al. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning[J]. Computers in Biology and Medicine, 137, 104806(2021).

[13] Ding P C, Li F. Diabetic retinopathy lesion segmentation based on hierarchical feature progressive fusion in retinal fundus images[J]. Chinese Journal of Lasers, 51, 2107107(2024).

[14] Miao L Y, Li F. Retinal vessel segmentation based on dynamic feature graph convolutional network[J]. Chinese Journal of Lasers, 51, 1507208(2024).

[15] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention- MICCAI 2015, 9351, 234-241(2015).

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

[17] Guan X, Yang G, Ye J M et al. 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework[J]. BMC Medical Imaging, 22, 6(2022).

[18] Gu Y, Lu X Q, Yang L D et al. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs[J]. Computers in Biology and Medicine, 103, 220-231(2018).

[19] Cao Y, Zhou W F, Zang M et al. MBANet: a 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images[J]. Biomedical Signal Processing and Control, 80, 104296(2023).

[20] Wang W X, Chen C, Ding M et al. TransBTS: multimodal brain tumor segmentation using Transformer[M]. Medical image computing and computer-assisted intervention- MICCAI 2021, 12901, 109-119(2021).

[21] Sangui S, Iqbal T, Chandra P C et al. 3D MRI segmentation using U-Net architecture for the detection of brain tumor[J]. Procedia Computer Science, 218, 542-553(2023).

[22] Lu X Q, Gu Y, Yang L D et al. Multi-level 3D densenets for false-positive reduction in lung nodule detection based on chest computed tomography[J]. Current Medical Imaging, 16, 1004-1021(2020).

[23] Chen C, Liu X P, Ding M et al. 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI[M]. Medical image computing and computer-assisted intervention- MICCAI 2019, 11766, 184-192(2019).

[24] Luo Z R, Jia Z D, Yuan Z M et al. HDC-Net: hierarchical decoupled convolution network for brain tumor segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 25, 737-745(2021).

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

[26] Li X T, You A S, Zhu Z et al. Semantic flow for fast and accurate scene parsing[M]. Computer vision-ECCV 2020, 12346, 775-793(2020).

[27] Lin W F, Wu Z H, Chen J Y et al. Scale-aware modulation meet transformer[C], 5992-6003(2023).

[28] Gu Y, Liu J Q, Yang L D et al. SSANet: novel residual network for computer-aided diagnosis of pulmonary nodules in chest computed tomography[J]. International Journal of Imaging Systems and Technology, 34, e23176(2024).

[29] Agrawal P, Katal N, Hooda N. Segmentation and classification of brain tumor using 3D-UNet deep neural networks[J]. International Journal of Cognitive Computing in Engineering, 3, 199-210(2022).

[30] Jiang Y, Zhang Y, Lin X et al. SwinBTS: a method for 3D multimodal brain tumor segmentation using Swin Transformer[J]. Brain Sciences, 12, 797(2022).

[31] Raza R, Bajwa U I, Mehmood Y et al. dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI[J]. Biomedical Signal Processing and Control, 79, 103861(2023).

[32] Wang P X, Liu S K, Peng J L. AST-Net: lightweight hybrid Transformer for multimodal brain tumor segmentation[C], 4623-4629(2022).

[33] Peng Y J, Sun J D. The multimodal MRI brain tumor segmentation based on AD-Net[J]. Biomedical Signal Processing and Control, 80, 104336(2023).

[34] Zhang R F, Jia S S, Adamu M J et al. HMNet: hierarchical multi-scale brain tumor segmentation network[J]. Journal of Clinical Medicine, 12, 538(2023).

[35] Song J L, Lü X Q, Gu Y. Brain tumor image segmentation based on semantic flow guided sampling and attention mechanism[J]. Optics and Precision Engineering, 32, 565-577(2024).