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A novel DAVnet3+ method for precise segmentation of bladder cancer in MRI

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Abstract

Muscular invasion is a critical factor in the surgical selection of bladder cancer (BC) patients. Precise segmentation of bladder outer wall (OW), bladder inner wall (IW) and bladder tumor (BT) using MRI is an essential step in the computer-assisted diagnosis of muscle invasiveness in BC. Our study is aimed to develop a novel deep learning-based model to segment the OW, IW and BT, which could effectively aid physicians in BC diagnosis. We proposed a Dual Attention Vnet triple plus (DAVnet3+) network for the accurate segmentation of BC MRI. To fully utilize the feature maps information, full-scale skip connections between down-sampling layers and up-sampling layers were added. Moreover, spatial and channel attention were introduced to increase the weight of the target region, while suppressing the irrelevant background regions for the segmentation task. Two datasets are used to validate the segmentation performance of the proposed DAVnet3+ model. Dataset1 comes from the 2018 China University Computer Design Competition and includes 2200 T2WI BC slices, of which 1760 slices are used for training and 440 slices for testing. Dataset2 contains 1545 T2WI slices provided by the Urology Department of the First Affiliated Hospital of Nanjing Medical University, of which 1236 slices are used for training and 309 slices for testing. Compared to the state-of-the-art deep learning-based methods, the DAVnet3+ model achieves the relatively best segmentation results in terms of dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) on both datasets. (Dataset1: IW: DSC = 0.9626 ± 0.0041, ASSD = 0.3327 ± 0.0363; OW: DSC = 0.8170 ± 0.0266, ASSD = 0.4001 ± 0.0847; BT: DSC = 0.8635 ± 0.0246, ASSD = 0.4884 ± 0.1521. Dataset2: IW: DSC = 0.9840 ± 0.0061, ASSD = 0.2772 ± 0.0268; OW: DSC = 0.8392 ± 0.0188, ASSD = 0.3363 ± 0.0681; BT: DSC = 0.9391 ± 0.0144, ASSD = 0.2921 ± 0.1154). Our findings suggest that DAVnet3+ network is capable of segmenting the OW, IW and BT accurately in T2-weighted MRI. Additionally, it also substantiates the feasibility of the deep learning-based model for multi-region segmentation of BC.

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Data availability statement

The dataset2 that support the findings of this study are available from the First Affiliated Hospital of Nanjing Medical University but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the First Affiliated Hospital of Nanjing Medical University.

References

  1. Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 69(1), 7–34 (2019). https://doi.org/10.3322/caac.21551

    Article  Google Scholar 

  2. Chamie, K., Litwin, M.S., Bassett, J.C., Daskivich, T.J., Lai, J., Hanley, J.M., Konety, B.R., Saigal, C.S.: Recurrence of high-risk bladder cancer: a population-based analysis. Cancer 119(17), 3219–3227 (2013). https://doi.org/10.1002/cncr.28147

    Article  Google Scholar 

  3. Kamat, A.M., Hahn, N.M., Efstathiou, J.A., Lerner, S.P., Malmström, P.U., Choi, W., Guo, C.C., Lotan, Y., Kassouf, W.: Bladder cancer. The Lancet 388(10061), 2796–2810 (2016). https://doi.org/10.1016/S0140-6736(16)30512-8

    Article  Google Scholar 

  4. Funt, S.A., Rosenberg, J.E.: Systemic, perioperative management of muscle-invasive bladder cancer and future horizons. Nat. Rev. Clin. Oncol. 14(4), 221–234 (2017)

    Article  Google Scholar 

  5. Karakiewicz, P.I., Shariat, S.F., Palapattu, G.S., Perrotte, P., Lotan, Y., Rogers, C.G., Amiel, G.E., Vazina, A., Bastian, P.J., Lerner, S.P.: Precystectomy nomogram for prediction of advanced bladder cancer stage. Eur. Urol. 50(6), 1254–1262 (2006). https://doi.org/10.1016/j.eururo.2006.06.010

    Article  Google Scholar 

  6. Svatek, R.S., Shariat, S.F., Novara, G., Skinner, E.C., Fradet, Y., Bastian, P.J., Kamat, A.M., Kassouf, W., Karakiewicz, P.I., Dinney, C.P.: Discrepancy between clinical and pathological stage: external validation of the impact on prognosis in an international radical cystectomy cohort. BJU Int. 107(6), 898–904 (2011). https://doi.org/10.1111/j.1464-410X.2010.09628.x

    Article  Google Scholar 

  7. Green, D.A., Rink, M., Hansen, J., Cha, E.K., Robinson, B., Tian, Z., Shariat, S.F.: Accurate preoperative prediction of non-organ-confined bladder urothelial carcinoma at cystectomy. BJU Int. 111(3), 404–411 (2013). https://doi.org/10.1111/j.1464-410X.2012.11370.x

    Article  Google Scholar 

  8. Xu, X., Zhang, X., Tian, Q., Wang, H., Cui, L.B., Li, S., Liu, Y., Tang, X., Li, B., Dolz, J., Ayed, I.: Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis. J. Magn. Reson. Imaging 49(5), 1489–1498 (2019). https://doi.org/10.1002/jmri.26327

    Article  Google Scholar 

  9. Witjes, J.A., Lebret, T., Compérat, E.M., Cowan, N.C., De Santis, M., Bruins, H.M., Hernandez, V., Espinos, E., Dunn, J., Ribal, M.J.: Updated 2016 EAU guidelines on muscle-invasive and metastatic bladder cancer. Eur. Urol. 71(3), 462–475 (2017). https://doi.org/10.1016/j.eururo.2016.06.020

    Article  Google Scholar 

  10. Verma, S., Rajesh, A., Prasad, S.R., Gaitonde, K., Lall, C.G., Mouraviev, V., Aeron, G., Bracken, R., Sandrasegaran, K.: Urinary bladder cancer: role of MR imaging. Radiographics 32(2), 371–387 (2012). https://doi.org/10.1148/rg.322115125

    Article  Google Scholar 

  11. Xiao, D., Zhang, G., Liu, Y., Yang, Z., Zhang, X., Li, L., Jiao, C., Lu, H.: 3D detection and extraction of bladder tumors via MR virtual cystoscopy. Int. J. Comput. Assist. Radiol. Surg. 11(1), 89–97 (2016)

    Article  Google Scholar 

  12. Qin, X., Li, X., Liu, Y., Lu, H., Yan, P.: Adaptive shape prior constrained level sets for bladder MR image segmentation. IEEE J. Biomed. Health Inform. 18(5), 1707–1716 (2013)

    Article  Google Scholar 

  13. Pinto, J.R., Tavares, J.M.R.: A versatile method for bladder segmentation in computed tomography two-dimensional images under adverse conditions. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 231(9), 871–880 (2017). https://doi.org/10.1177/0954411917714294

    Article  Google Scholar 

  14. Pizzi, A.D., Mastrodicasa, D., Marchioni, M., Primiceri, G., Difabio, F., Cianci, R., Seccia, B., Sessa, B., Mincuzzi, E., Caulo, M.: Bladder cancer: do we need contrast injection for MRI assessment of muscle invasion? A prospective multi-reader VI-RADS approach. Eur. Radiol. 31(6), 3874–3883 (2021)

    Article  Google Scholar 

  15. Ueno, Y., Takeuchi, M., Tamada, T., Sofue, K., Takahashi, S., Kamishima, Y., Hinata, N., Harada, K., Fujisawa, M., Murakami, T.: Diagnostic accuracy and interobserver agreement for the vesical imaging-reporting and data system for muscle-invasive bladder cancer: a multireader validation study. Eur. Urol. 76(1), 54–56 (2019)

    Article  Google Scholar 

  16. McKibben, M.J., Woods, M.E.: Preoperative imaging for staging bladder cancer. Curr. Urol. Rep. 16(4), 22 (2015)

    Article  Google Scholar 

  17. Duan, C., Liang, Z., Bao, S., Zhu, H., Wang, S., Zhang, G., Chen, J., Lu, H.: A coupled level set framework for bladder wall segmentation with application to MR cystography. IEEE Trans. Med. Imaging 29(3), 903–915 (2010)

    Article  Google Scholar 

  18. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28

  19. Cheng, Z., Qu, A., He, X.: Contour-aware semantic segmentation network with spatial attention mechanism for medical image. Vis. Comput. 38(3), 749–762 (2022). https://doi.org/10.1007/s00371-021-02075-9

    Article  Google Scholar 

  20. Chen, X., Jiang, S., Guo, L., Chen, Z., Zhang, C.: Whole brain segmentation method from 2.5 D brain MRI slice image based on triple U-Net. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02326-9

    Article  Google Scholar 

  21. Li, D., Peng, L., Peng, S., Xiao, H., Zhang, Y.: Retinal vessel segmentation by using AFNet. Vis. Comput. 1, 13 (2022). https://doi.org/10.1007/s00371-022-02456-8

    Article  Google Scholar 

  22. Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y., Wu, J.: Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1055–1059 (2020)

  23. Dolz, J., Xu, X., Rony, J., Yuan, J., Liu, Y., Granger, E., Desrosiers, C., Zhang, X., Ben, A.I., Lu, H.: Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks. Med. Phys. 45(12), 5482–5493 (2018). https://doi.org/10.1002/mp.13240

    Article  Google Scholar 

  24. Liu, J., Liu, L., Xu, B., Hou, X., Liu, B., Chen, X., Shen, L., Qiu, G.: Bladder cancer multi-class segmentation in mri with pyramid-in-pyramid network. In: 2019 IEEE 16th International Symposium on Biomedical Imaging, pp. 28–31 (2019)

  25. Hammouda, K., Khalifa, F., Soliman, A., Ghazal, M., Abou, El-Ghar, M., Haddad, A., Elmogy, M., Darwish, H.E., Khalil, A., El-Baz, A.: A CNN-based framework for bladder wall segmentation using MRI. In: 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), pp. 1–4 (2019)

  26. Ge, R., Cai, H., Yuan, X., Qin, F., Huang, Y., Wang, P., Lyu, L.: MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer. Comput. Biol. Chem. 93, 107510 (2021). https://doi.org/10.1016/j.compbiolchem.2021.107510

    Article  Google Scholar 

  27. Isensee, F., Jaeger, P.F., Kohl, S.A.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)

    Article  Google Scholar 

  28. Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016)

  29. Fang, Y., Huang, H., Yang, W., Xu, X.M., Jiang, W.W., Lai, X.B.: Nonlocal convolutional block attention module VNet for gliomas automatic segmentation. Int. J. Imaging Syst. Technol. 32(2), 528–543 (2022)

    Article  Google Scholar 

  30. Liu, Y., Yang, Y., Jiang, W., Wang, T.F., Lei, B.Y.: Semi-supervised attention-guided VNet for breast cancer detection via multi-task learning. In: International Conference on Image and Graphics, pp. 559–570 (2021)

  31. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

  32. Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., Glocker, B., Rueckert, D.: Attention u-net: Learning where to look for the pancreas. arXiv:1804.03999 (2018)

  33. Li, C., Tan, Y., Chen, W., Luo, X., He, Y., Gao, Y., Li, F.: ANU-Net: attention-based nested U-net to exploit full resolution features for medical image segmentation. Comput. Graph. 90, 11–20 (2020). https://doi.org/10.1016/j.cag.2020.05.003

    Article  Google Scholar 

  34. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

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Funding

This study was supported by the National Natural Science Foundation of China (Grant Nos.12071215).

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  1. Liang Wang and Lingkai Cai have contributed equally to this work.

Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Liang Wang, Chunxiao Chen, Xue Fu & Jie Yu

  2. Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    Lingkai Cai, Baorui Yuan, Xiao Yang & Qiang Lv

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Rongjun Ge

  4. Department of Urology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China

    Qiang Shao

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Wang, L., Cai, L., Chen, C. et al. A novel DAVnet3+ method for precise segmentation of bladder cancer in MRI. Vis Comput 39, 4737–4749 (2023). https://doi.org/10.1007/s00371-022-02622-y

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