Kaixiong Yang and Yanni Zou
[1] S. Jardim, J. Ant´onio, and C. Mora, Image thresholdingapproaches for medical image segmentation-short literaturereview, Procedia Computer Science, 219, 2023, 1485–1492. [2] C. Qin, Y. Wang, and J. Zhang, URCA: Uncertainty-based region clipping algorithm for semi-supervised medicalimage segmentation, Computer Methods and Programs inBiomedicine, 254, 2024, 108278. [3] Y. Zhang, G. Balestra, K. Zhang, J. Wang, S. Rosati, andV. Giannini, Multitrans: Multi-branch transformer network formedical image segmentation, Computer Methods and Programsin Biomedicine, 254, 2024, 108280. [4] Y. Jiang, B. Liu, Z. Zhang, Y. Yan, H. Guo, and Y. Li,Dense-sparse representation matters: A point-based methodfor volumetric medical image segmentation, Journal of VisualCommunication and Image Representation, 100, 2024, 104115. [5] M. Aljabri and M. AlGhamdi, A review on the use of deeplearning for medical images segmentation, Neurocomputing,506, 2022, 311–335. [6] G. Wang, W. Li, M. Aertsen, J. Deprest, S. Ourselin, andT. Vercauteren, Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation withconvolutional neural networks, Neurocomputing, 338, 2019,34–45. [7] S. Zhou, D. Nie, E. Adeli, J. Yin, J. Lian, and D. Shen, High-resolution encoder–decoder networks for low-contrast medicalimage segmentation, IEEE Transactions on Image Processing,29, 2019, 461–475. [8] R. Adams and L. Bischof, Seeded region growing, IEEETransactions on Pattern Analysis and Machine Intelligence,16, 1994, 641–647. [9] Y.-Z. Zeng, S.-H. Liao, P. Tang, Y.-Q. Zhao, M. Liao, Y. Chen,and Y.-X. Liang, Automatic liver vessel segmentation using 3Dregion growing and hybrid active contour model, Computersin Biology and Medicine, 97, 2018, 63–73. [10] X. Yang, J. Do Yang, H.P. Hwang, H.C. Yu, S. Ahn, B.-W.Kim, and H. You, Segmentation of liver and vessels from CTimages and classification of liver segments for preoperativeliver surgical planning in living donor liver transplantation,Computer Methods and Programs in Biomedicine, 158, 2018,41–52. [11] Y. Zhang and P.X. Liu, Dual average twin delayed deepdeterministic policy gradient (DATD3): Addressing estimationbias in deep reinforcement learning, International Journal ofRobotics and Automation, 40, 2025 374–382. [12] Z. Xing, X. Zhu, and Y. Wu, A new real-time 3D dense semanticmapping system for large-scale environments, InternationalJournal of Robotics and Automation, 39, 2024, 12–23. [13] S. Gao, N. Zhang, Y. Liu, and J. Pan, Multimodal fusionmethod for knowledge extraction and inference of agriculturalrobots, International Journal of Robotics and Automation, 40,2025, 321–333. [14] S. Zhou, J. Wang, S. Zhang, Y. Liang, and Y. Gong, Activecontour model based on local and global intensity informationfor medical image segmentation, Neurocomputing 186, 2016,107–118. [15] J. Long, E. Shelhamer, and T. Darrell, Fully convolutionalnetworks for semantic segmentation, in Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, 2015,3431–3440. [16] O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutionalnetworks for biomedical image segmentation, in Proceedings ofInternational Conference on Medical Image Computing andComputer-Assisted Intervention, 2015, 234–241. [17] H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, andM. Wang, Swin-Unet: Unet-like pure transformer for medicalimage segmentation, in Proceedings of European Conferenceon Computer Vision, Cham, 2022, 1–4. [18] C. Guo, M. Szemenyei, Y. Yi, W. Wang, B. Chen, andC. Fan, SA-UNet: Spatial attention u-net for retinal vesselsegmentation, in Proceedings of 2020 25th InternationalConference on Pattern Recognition (ICPR), 2021, 1236–1242. [19] S. Chen, Y. Zou, and P.X. Liu, IBA-U-Net: AttentiveBConvLSTM U-Net with redesigned inception for medicalimage segmentation, Computers in Biology and Medicine, 135,2021, 104551. [20] S. Cai, Y. Tian, H. Lui, H. Zeng, Y. Wu, and G. Chen, Dense-UNet: A novel multiphoton in vivo cellular image segmentationmodel based on a convolutional neural network, QuantitativeImaging in Medicine and Surgery, 10(6), 2020, 1275–1285. [21] D. Maji, P. Sigedar, and M. Singh, Attention Res-UNet withguided decoder for semantic segmentation of brain tumors,Biomedical Signal Processing and Control, 71, 2022, 103077. [22] W. Yu, B. Fang, Y. Liu, M. Gao, S. Zheng, and Y. Wang,Liver vessels segmentation based on 3D residual U-Net, inProceedings of 2019 IEEE International Conference on ImageProcessing (ICIP), 2019, 250–254.12 [23] Q. Huang, J. Sun, H. Ding, X. Wang, and G. Wang, Robustliver vessel extraction using 3D U-Net with variant dice lossfunction, Computers in Biology and Medicine, 101, 2018,153–162. [24] T. Kitrungrotsakul, X.-H. Han, Y. Iwamoto, L. Lin, A.H.Foruzan, W. Xiong, and Y.-W. Chen, VesselNet: A deepconvolutional neural network with multi pathways for robusthepatic vessel segmentation, Computerized Medical Imagingand Graphics, 75, 2019, 74–83. [25] W. Hao, J. Zhang, J. Su, Y. Song, Z. Liu, Y. Liu,C. Qiu, and K. Han, HPM-Net: Hierarchical progressivemultiscale network for liver vessel segmentation in CT images,Computer Methods and Programs in Biomedicine, 224, 2022,107003. [26] ¨O. C¸i¸cek, A. Abdulkadir, S.S. Lienkamp, T. Brox,and O. Ronneberger, 3D U-Net: Learning densevolumetric segmentation from sparse annotation, inProceedings of International Conference on Medical ImageComputing and Computer-Assisted Intervention, 2016,424–432. [27] J. Chen, J. Mei, X. Li, Y. Lu, Q. Yu, Q. Wei, X. Luo, Y.Xie, E. Adeli, Y. Wang, M.P. Lungren, S. Zhang, L. Xing, L.Lu, A. Yuille, and Y. Zhou, TransUNet: Rethinking the U-Netarchitecture design for medical image segmentation throughthe lens of transformers, Medical Image Analysis, 97, 2024,103280. [28] Cai, Y. Long, Z. Han, M. Liu, Y. Zheng, W. Yang,and L. Chen, Swin UNet3D: A three-dimensional medicalimage segmentation network combining vision transformer andconvolution, BMC Medical Informatics and Decision Making,23, 2023, 33. [29] L. Zhang, X. Yin, X. Liu, and Z. Liu, Medical image segmenta-tion by combining feature enhancement swin transformer andUperNet, Scientific Reports, 15, 2025, 14565. [30] Y. Zhang, C. Peng, L. Peng, H. Huang, R. Tong, L. Lin,J. Li, Y.-W. Chen, Q. Chen, H. Hu, and Z. Peng, Multi-phase liver tumor segmentation with spatial aggregation anduncertain region inpainting, in Proceedings of InternationalConference on Medical Image Computing and Computer-Assisted Intervention, Cham, 2021, 68–77. [31] H. Zhang, K. Zu, J. Lu, Y. Zou, and D. Meng, EPSANet:An efficient pyramid squeeze attention block on convolutionalneural network, in Proceedings of the Asian Conference onComputer Vision, 2022, 541–557. [32] J. Hu, L. Shen, and G. Sun, Squeeze-and-excitation networks,in Proceedings of the IEEE conference on Computer Visionand Pattern Recognition, 2018, 7132–7141. [33] D. Kinga and J.B. Adam, A method for stochastic optimization,in Proceedings of International Conference on LearningRepresentations (ICLR), 2015, 1–15. [34] F. Milletari, N. Navab, and S.-A. Ahmadi, V-Net: Fullyconvolutional neural networks for volumetric medical imagesegmentation, in Proceedings of 2016 Fourth InternationalConference on 3D Vision (3DV), 2016, 565–571. [35] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K.Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest,and L. Lanczi, The multimodal brain tumor image segmentationbenchmark (BRATS), IEEE Transactions on Medical Imaging,34(10), 2014, 1993–2024. [36] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki,J.S. Kirby, J.B. Freymann, K. Farahani, and C. Davatzikos,Advancing the cancer genome atlas glioma MRI collections withexpert segmentation labels and radiomic features, ScientificData, 4(1), 2017, 1–13. [37] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler,A. Crimi, R.T. Shinohara, C. Berger, S. M. Ha, and M.Rozycki, Identifying the best machine learning algorithms forbrain tumor segmentation, progression assessment, and overallsurvival prediction in the BRATS challenge, arXiv:1811.02629,2018. [38] X. Hu, H. Li, Y. Zhao, C. Dong, B. H. Menze, and M. Piraud,Hierarchical multi-class segmentation of glioma images usingnetworks with multi- level activation function, in Proceedingsof International MICCAI Brainlesion Workshop, Cham, 2018,1–12. [39] J. Serrano-Rubio, R. Everson, and H. Hutt, Brain tumoursegmentation method based on sparse feature vectors, inProceedings of the 7th MICCAI BraTS Challenge, Granada,2018 58533–58545. [40] J. Zhang, Z. Jiang, J. Dong, Y. Hou, and B. Liu, Attentiongate ResU-Net for automatic MRI brain tumor segmentation,IEEE Access, 8, 2020, 58533–58545. [41] R. Mehta and T. Arbel, 3D U-Net for brain tumoursegmentation, in Proceedings of International MICCAIBrainlesion Workshop, Cham, 2018, 254–266. [42] A. Kermi, I. Mahmoudi, and M. T. Khadir, Deep convolutionalneural networks using u-net for automatic brain tumorsegmentation in multimodal MRI volumes, in Proceedings ofInternational MICCAI Brainlesion Workshop, Cham, 2018,37–48. [43] N.M. Aboelenein, P. Songhao, A. Koubaa, A. Noor, and A.Afifi, HTTU-Net: Hybrid two track U-Net for automatic braintumor segmentation, IEEE Access, 8, 2020, 101406–101415. [44] W. Chen, B. Liu, S. Peng, J. Sun, and X. Qiao, S3D-UNet: Separable 3D U-Net for brain tumor segmentation, inProceedings of International MICCAI Brainlesion Workshop,Cham, 2018, 358–368. [45] I. Aboussaleh, J. Riffi, K.E. Fazazy, M.A. Mahraz, and H.Tairi, Efficient U-Net architecture with multiple encoders andattention mechanism decoders for brain tumor segmentation,Diagnostics, 13(5), 2023, 872. [46] Y. Qiu, D. Chen, H. Yao, Y. Xu, and Z. Wang, Scratcheach other’s back: Incomplete multi-modal brain tumorsegmentation via category aware group self-support learning,in Proceedings of the IEEE/CVF International Conference onComputer Vision, 2023, 1–10.
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