{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T17:40:44Z","timestamp":1770140444737,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12071215"],"award-info":[{"award-number":["12071215"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Scientific resrarch project of Gusu College of Nanjing Medical University","award":["20210222"],"award-info":[{"award-number":["20210222"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00371-022-02622-y","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T15:06:12Z","timestamp":1661526372000},"page":"4737-4749","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A novel DAVnet3+ method for precise segmentation of bladder cancer in MRI"],"prefix":"10.1007","volume":"39","author":[{"given":"Liang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingkai","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunxiao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongjun","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baorui","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"issue":"1","key":"2622_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21551","volume":"69","author":"RL Siegel","year":"2019","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 69(1), 7\u201334 (2019). https:\/\/doi.org\/10.3322\/caac.21551","journal-title":"CA Cancer J. Clin."},{"issue":"17","key":"2622_CR2","doi-asserted-by":"publisher","first-page":"3219","DOI":"10.1002\/cncr.28147","volume":"119","author":"K Chamie","year":"2013","unstructured":"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\u20133227 (2013). https:\/\/doi.org\/10.1002\/cncr.28147","journal-title":"Cancer"},{"issue":"10061","key":"2622_CR3","doi-asserted-by":"publisher","first-page":"2796","DOI":"10.1016\/S0140-6736(16)30512-8","volume":"388","author":"AM Kamat","year":"2016","unstructured":"Kamat, A.M., Hahn, N.M., Efstathiou, J.A., Lerner, S.P., Malmstr\u00f6m, P.U., Choi, W., Guo, C.C., Lotan, Y., Kassouf, W.: Bladder cancer. The Lancet 388(10061), 2796\u20132810 (2016). https:\/\/doi.org\/10.1016\/S0140-6736(16)30512-8","journal-title":"The Lancet"},{"issue":"4","key":"2622_CR4","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/nrclinonc.2016.188","volume":"14","author":"SA Funt","year":"2017","unstructured":"Funt, S.A., Rosenberg, J.E.: Systemic, perioperative management of muscle-invasive bladder cancer and future horizons. Nat. Rev. Clin. Oncol. 14(4), 221\u2013234 (2017)","journal-title":"Nat. Rev. Clin. Oncol."},{"issue":"6","key":"2622_CR5","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1016\/j.eururo.2006.06.010","volume":"50","author":"PI Karakiewicz","year":"2006","unstructured":"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\u20131262 (2006). https:\/\/doi.org\/10.1016\/j.eururo.2006.06.010","journal-title":"Eur. Urol."},{"issue":"6","key":"2622_CR6","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1111\/j.1464-410X.2010.09628.x","volume":"107","author":"RS Svatek","year":"2011","unstructured":"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\u2013904 (2011). https:\/\/doi.org\/10.1111\/j.1464-410X.2010.09628.x","journal-title":"BJU Int."},{"issue":"3","key":"2622_CR7","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1111\/j.1464-410X.2012.11370.x","volume":"111","author":"DA Green","year":"2013","unstructured":"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\u2013411 (2013). https:\/\/doi.org\/10.1111\/j.1464-410X.2012.11370.x","journal-title":"BJU Int."},{"issue":"5","key":"2622_CR8","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1002\/jmri.26327","volume":"49","author":"X Xu","year":"2019","unstructured":"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\u20131498 (2019). https:\/\/doi.org\/10.1002\/jmri.26327","journal-title":"J. Magn. Reson. Imaging"},{"issue":"3","key":"2622_CR9","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.eururo.2016.06.020","volume":"71","author":"JA Witjes","year":"2017","unstructured":"Witjes, J.A., Lebret, T., Comp\u00e9rat, 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\u2013475 (2017). https:\/\/doi.org\/10.1016\/j.eururo.2016.06.020","journal-title":"Eur. Urol."},{"issue":"2","key":"2622_CR10","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1148\/rg.322115125","volume":"32","author":"S Verma","year":"2012","unstructured":"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\u2013387 (2012). https:\/\/doi.org\/10.1148\/rg.322115125","journal-title":"Radiographics"},{"issue":"1","key":"2622_CR11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s11548-015-1234-x","volume":"11","author":"D Xiao","year":"2016","unstructured":"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\u201397 (2016)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"5","key":"2622_CR12","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1109\/JBHI.2013.2288935","volume":"18","author":"X Qin","year":"2013","unstructured":"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\u20131716 (2013)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"9","key":"2622_CR13","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1177\/0954411917714294","volume":"231","author":"JR Pinto","year":"2017","unstructured":"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\u2013880 (2017). https:\/\/doi.org\/10.1177\/0954411917714294","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"issue":"6","key":"2622_CR14","doi-asserted-by":"publisher","first-page":"3874","DOI":"10.1007\/s00330-020-07473-6","volume":"31","author":"AD Pizzi","year":"2021","unstructured":"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\u20133883 (2021)","journal-title":"Eur. Radiol."},{"issue":"1","key":"2622_CR15","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.eururo.2019.03.012","volume":"76","author":"Y Ueno","year":"2019","unstructured":"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\u201356 (2019)","journal-title":"Eur. Urol."},{"issue":"4","key":"2622_CR16","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s11934-015-0496-8","volume":"16","author":"MJ McKibben","year":"2015","unstructured":"McKibben, M.J., Woods, M.E.: Preoperative imaging for staging bladder cancer. Curr. Urol. Rep. 16(4), 22 (2015)","journal-title":"Curr. Urol. Rep."},{"issue":"3","key":"2622_CR17","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2009.2039756","volume":"29","author":"C Duan","year":"2010","unstructured":"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\u2013915 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2622_CR18","doi-asserted-by":"publisher","unstructured":"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\u2013241 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"2622_CR19","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s00371-021-02075-9","volume":"38","author":"Z Cheng","year":"2022","unstructured":"Cheng, Z., Qu, A., He, X.: Contour-aware semantic segmentation network with spatial attention mechanism for medical image. Vis. Comput. 38(3), 749\u2013762 (2022). https:\/\/doi.org\/10.1007\/s00371-021-02075-9","journal-title":"Vis. Comput."},{"key":"2622_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02326-9","author":"X Chen","year":"2021","unstructured":"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","journal-title":"Vis. Comput."},{"key":"2622_CR21","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00371-022-02456-8","volume":"1","author":"D Li","year":"2022","unstructured":"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","journal-title":"Vis. Comput."},{"key":"2622_CR22","doi-asserted-by":"crossref","unstructured":"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\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"issue":"12","key":"2622_CR23","doi-asserted-by":"publisher","first-page":"5482","DOI":"10.1002\/mp.13240","volume":"45","author":"J Dolz","year":"2018","unstructured":"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\u20135493 (2018). https:\/\/doi.org\/10.1002\/mp.13240","journal-title":"Med. Phys."},{"key":"2622_CR24","doi-asserted-by":"crossref","unstructured":"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:\u00a02019 IEEE 16th International Symposium on Biomedical Imaging, pp. 28\u201331 (2019)","DOI":"10.1109\/ISBI.2019.8759422"},{"key":"2622_CR25","doi-asserted-by":"crossref","unstructured":"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\u20134 (2019)","DOI":"10.1109\/ICABME47164.2019.8940266"},{"key":"2622_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2021.107510","volume":"93","author":"R Ge","year":"2021","unstructured":"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","journal-title":"Comput. Biol. Chem."},{"issue":"2","key":"2622_CR27","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"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\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"2622_CR28","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In:\u00a02016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"issue":"2","key":"2622_CR29","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1002\/ima.22639","volume":"32","author":"Y Fang","year":"2022","unstructured":"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\u2013543 (2022)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"2622_CR30","doi-asserted-by":"crossref","unstructured":"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\u2013570 (2021)","DOI":"10.1007\/978-3-030-87358-5_45"},{"key":"2622_CR31","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In:\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2622_CR32","unstructured":"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)"},{"key":"2622_CR33","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cag.2020.05.003","volume":"90","author":"C Li","year":"2020","unstructured":"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\u201320 (2020). https:\/\/doi.org\/10.1016\/j.cag.2020.05.003","journal-title":"Comput. Graph."},{"key":"2622_CR34","doi-asserted-by":"crossref","unstructured":"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\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02622-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-022-02622-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02622-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:14:42Z","timestamp":1695978882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-022-02622-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,26]]},"references-count":34,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["2622"],"URL":"https:\/\/doi.org\/10.1007\/s00371-022-02622-y","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,26]]},"assertion":[{"value":"11 July 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}