Abstract
Segmenting the urinary collecting system based on preoperative contrast-enhanced computed tomography urography volumes is necessary for assisting flexible ureterorenoscopy. The urinary collecting system consists of complex elongated tubular structures and irregular tree-like structures, making it challenging for precise segmentations using current deep-learning-based methods. Existing deep learning-driven methods face challenges in accurately segmenting the urinary collecting system from contrast-enhanced computed tomography urography volumes. In this work, we propose a novel MixU-Net by embedding global feature mix blocks. Particularly, the global feature mix blocks allow wider receptive fields based on fused multi-layer-perception and 3D convolutions across different dimensions. The experimental validations on the clinical computed tomography urography volumes demonstrate that our method achieves state-of-the-art in terms of dice similarity coefficients, intersection over union, and Hausdorff distance when compared with other methods that use pure convolutional neural networks or hybrid convolutional neural networks and Transformers. In addition, preliminary experiments conducted on the navigation system demonstrate the improved accuracy of the virtual depth maps when adopting the segmented urinary collecting system obtained by our MixU-Net.
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References
Breda, A., Ogunyemi, O., Leppert, J.T., Schulam, P.G.: Flexible ureteroscopy and laser lithotripsy for multiple unilateral intrarenal stones. Eur. Urol. 55(5), 1190–1197 (2009)
Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol. 13803, pp. 205–218. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25066-8_9
Cardoso, M.J., et al.: MONAI: an open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022)
Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Cho, S.Y.: Current status of flexible ureteroscopy in urology. Korean J. Urol. 56(10), 680–688 (2015)
Cho, S.Y., et al.: Cumulative sum analysis for experiences of a single-session retrograde intrarenal stone surgery and analysis of predictors for stone-free status. PLoS ONE 9(1), e84878 (2014)
Ding, X., Zhang, X., Han, J., Ding, G.: Scaling up your kernels to 31\(\times \)31: revisiting large kernel design in CNNs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11963–11975 (2022)
Dong, Z., et al.: MNet: rethinking 2D/3D networks for anisotropic medical image segmentation. arXiv preprint arXiv:2205.04846 (2022)
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
El-Melegy, M., Kamel, R., El-Ghar, M.A., Shehata, M., Khalifa, F., El-Baz, A.: Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling. Sci. Rep. 12(1), 18816 (2022)
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol. 12962, pp. 272–284. Springer (2022). https://doi.org/10.1007/978-3-031-08999-2_22
Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the kits19 challenge. Med. Image Anal. 67, 101821 (2021)
Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Kim, T., et al.: Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: evaluation on kidney segmentation in abdominal CT. Sci. Rep. 10(1), 366 (2020)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)
Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Miller, N.L., Lingeman, J.E.: Management of kidney stones. Bmj 334(7591), 468–472 (2007)
Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)
Rule, A.D., Bergstralh, E.J., Melton, L.J., Li, X., Weaver, A.L., Lieske, J.C.: Kidney stones and the risk for chronic kidney disease. Clin. J. Am. Soc. Nephrol. 4(4), 804–811 (2009)
Taha, A., Lo, P., Li, J., Zhao, T.: Kid-Net: convolution networks for kidney vessels segmentation from CT-volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 463–471. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_53
Tolstikhin, I.O., et al.: MLP-Mixer: An all-MLP architecture for vision. Adv. Neural. Inf. Process. Syst. 34, 24261–24272 (2021)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wu, Y., He, K.: Group normalization. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Xia, K.j., Yin, H.s., Zhang, Y.d.: Deep semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow algorithm. J. Med. Syst. 43, 1–12 (2019)
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant No. 62001403 and 61971367), Natural Science Foundation of Fujian Province of China (No. 2020J05003 and 2020J01004), and the Fujian Provincial Technology Innovation Joint Funds under Grant 2019Y9091
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Liu, Z. et al. (2024). MixU-Net: Hybrid CNN-MLP Networks for Urinary Collecting System Segmentation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_37
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DOI: https://doi.org/10.1007/978-981-99-8469-5_37
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