REFERENCES

1. Shao, P.; Qin, C.; Yin, C.; et al. Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes. Eur. Urol. 2011, 59, 849-55.

2. Yu, F. , Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv 2015, arXiv:1511.07122. https://arxiv.org/abs/1511.07122. (accessed 10 Sep 2025).

3. Dai, J.; Qi, H.; Xiong, Y.; et al. Deformable convolutional networks. In 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy. October 22-29, 2017. IEEE; 2017. pp. 764-73.

4. Qi, Y.; He, Y.; Qi, X.; Zhang, Y.; Yang, G. Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France. October 01-06, 2023. IEEE; 2023. pp 6047-56.

5. Zou, S.; Xiong, F.; Luo, H.; Lu, J.; Qian, Y. AF-Net: all-scale feature fusion network for road extraction from remote sensing images. In 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia. Novermber 29 - December 01, 2021. IEEE; 2021. pp 66-73.

6. Xian, Y.; Zhao, G.; Chen, X.; Wang, C. DCFU-Net: rethinking an effective attention and convolutional architecture for retinal vessel segmentation. Int. J. Imaging. Syst. Tech. 2025, 35, e70003.

7. Yang, C.; Zhang, H.; Chi, D.; et al. Contour attention network for cerebrovascular segmentation from TOF-MRA volumetric images. Med. Phys. 2024, 51, 2020-31.

8. Jalali, Y.; Fateh, M.; Rezvani, M. VGA-Net: vessel graph based attentional U-Net for retinal vessel segmentation. IET. Image. Processing. 2024, 18, 2191-213.

9. Shit, S.; Paetzold, J. C.; Sekuboyina, A.; et al. clDice - a novel topology-preserving loss function for tubular structure segmentation. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Nashiville, USA. June 20-25, 2021. IEEE; 2021. pp. 16560-9.

10. Wang, Y.; Wei, X.; Liu, F.; et al. Deep distance transform for tubular structure segmentation in CT scans. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA. June 13-19, 2020. IEEE; 2020. pp. 3832-41.

11. Shang, Z.; Yu, C.; Huang, H.; Li, R. DCNet: a lightweight retinal vessel segmentation network. Digit. Signal. Process. 2024, 153, 104651.

12. Pang, Y.; Liang, J.; Huang, T.; et al. Slim UNETR: scale hybrid transformers to efficient 3D medical image segmentation under limited computational resources. IEEE. Trans. Med. Imaging. 2024, 43, 994-1005.

13. Ronneberger, O.; Fischer, P.; Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Munich, Germany. Springer, Cham; 2015. pp. 234-41.

14. Zhang, Z.; Liu, Q.; Wang, Y. Road extraction by deep residual U-Net. IEEE. Geosci. Remote. Sensing. Lett. 2018, 15, 749-53.

15. Zhou, Z.; Rahman, Siddiquee. M. M.; Tajbakhsh, N.; Liang, J. UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov D, Taylor Z, Carneiro G, et al. editors. Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer International Publishing; 2018. pp. 3-11.

16. Huang, H.; Lin, L.; Tong, R.; et al. Unet 3+: a full-scale connected UNet for medical image segmentation. In ICASSP 2020 - 2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), Barcelona, Spain. May 04-08, 2020. IEEE; 2020. pp. 1055-9.

17. Li, J.; Lo, P.; Taha, A.; Wu, H.; Zhao, T. Segmentation of renal structures for image-guided surgery. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical image computing and computer assisted intervention - MICCAI 2018. Cham: Springer International Publishing; 2018. pp. 454-62.

18. Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; et al. An image is worth 16 × 16 words: transformers for image recognition at scale. arXiv 2020, arXiv:2010.11929. https://arxiv. org/abs/2010.11929. (accessed 10 Sep 2025).

19. Liu, Z.; Lin, Y.; Cao, Y.; et al. Swin transformer: hierarchical vision transformer using shifted windows. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, Canada. October 10-17, 2021. IEEE; 2021. pp. 10012-22.

20. Chollet, F. Xception: deep learning with depthwise separable convolutions. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA. July 21-26, 2017. IEEE; 2017. pp. 1251-8.

21. Ho, J.; Kalchbrenner, N.; Weissenborn, D.; et al. Axial attention in multidimensional transformers. arXiv 2019, arXiv:1912.12180. https://arxiv.org/abs/1912.12180. (accessed 10 Sep 2025).

22. He, Y.; Yang, G.; Yang, J.; et al. Meta grayscale adaptive network for 3D integrated renal structures segmentation. Med. Image. Anal. 2021, 71, 102055.

23. He, Y.; Yang, G.; Yang, J.; et al. Dense biased networks with deep priori anatomy and hard region adaptation: semi-supervised learning for fine renal artery segmentation. Med. Image. Anal. 2020, 63, 101722.

24. Shao, P.; Tang, L.; Li, P.; et al. Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy. Eur. Urol. 2012, 62, 1001-8.

25. Grand Challenge. Kidney Parsing Challenge 2022. https://kipa22.grand-challenge.org/. (accessed 2025-09-10).

26. Staal, J.; Abràmoff, M. D.; Niemeijer, M.; et al. Ridge-based vessel segmentation in color images of the retina. IEEE. Trans. Med. Imaging. 2004, 23, 501-9.

27. Hoover, A.; Kouznetsova, V.; Goldbaum, M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE. Trans. Med. Imaging. 2000, 19, 203-10.

28. Fraz, M. M.; Remagnino, P.; Hoppe, A.; et al. An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE. Trans. Biomed. Eng. 2012, 59, 2538-48.

29. Kaggle. Satellite-road-segmentation-dataset. https://www.kaggle.com/datasets/timothlaborie/roadsegmentation-boston-losangeles/. (accessed 2025-09-10).

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