{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:36:11Z","timestamp":1743046571593,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031733598"},{"type":"electronic","value":"9783031733604"}],"license":[{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73360-4_8","type":"book-chapter","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T15:02:10Z","timestamp":1728054130000},"page":"70-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Single-Source Domain Generalization via\u00a0Anatomy-Guided Texture Augmentation for\u00a0Cervical Tumor Segmentation"],"prefix":"10.1007","author":[{"given":"Lixue","family":"Qin","sequence":"first","affiliation":[]},{"given":"Zhibo","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Nazar","family":"Zaki","sequence":"additional","affiliation":[]},{"given":"Yaoqin","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Wenjian","family":"Qin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,5]]},"reference":[{"key":"8_CR1","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-031-16443-9_15","volume-title":"MICCAI 2022","author":"C Chen","year":"2022","unstructured":"Chen, C., Li, Z., Ouyang, C., Sinclair, M., Bai, W., Rueckert, D.: MaxStyle: adversarial style composition for robust medical image segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 151\u2013161. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_15"},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26, 1045\u20131057 (2013)","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"8_CR3","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1186\/s12885-022-09826-4","volume":"22","author":"Z He","year":"2022","unstructured":"He, Z., et al.: The value of HPV genotypes combined with clinical indicators in the classification of cervical squamous cell carcinoma and adenocarcinoma. BMC Cancer 22(1), 776 (2022)","journal-title":"BMC Cancer"},{"key":"8_CR4","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-031-45087-7_4","volume-title":"CMMCA 2023","author":"Z He","year":"2023","unstructured":"He, Z., Lv, F., Li, C., Liu, Y., Xiao, Z.: The value of ensemble learning model based on conventional non-contrast MRI in the pathological grading of cervical cancer. In: Qin, W., Zaki, N., Zhang, F., Wu, J., Yang, F., Li, C. (eds.) CMMCA 2023. LNCS, vol. 14243, pp. 31\u201341. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-45087-7_4"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Hu, S., Liao, Z., Xia, Y.: Domain specific convolution and high frequency reconstruction based unsupervised domain adaptation for medical image segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13437, pp. 650\u2013659. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_62","DOI":"10.1007\/978-3-031-16449-1_62"},{"issue":"1","key":"8_CR6","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1109\/TMI.2022.3210133","volume":"42","author":"S Hu","year":"2022","unstructured":"Hu, S., Liao, Z., Zhang, J., Xia, Y.: Domain and content adaptive convolution based multi-source domain generalization for medical image segmentation. IEEE Trans. Med. Imaging 42(1), 233\u2013244 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"8_CR7","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1093\/jrr\/rrab070","volume":"62","author":"Y Kano","year":"2021","unstructured":"Kano, Y., Ikushima, H., Sasaki, M., Haga, A.: Automatic contour segmentation of cervical cancer using artificial intelligence. J. Radiat. Res. 62(5), 934\u2013944 (2021)","journal-title":"J. Radiat. Res."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Kim, S., Kim, D.H., Kim, H.: Texture learning domain randomization for domain generalized segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 677\u2013687 (2023)","DOI":"10.1109\/ICCV51070.2023.00069"},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s00330-019-06467-3","volume":"30","author":"YC Lin","year":"2020","unstructured":"Lin, Y.C., et al.: Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. Eur. Radiol. 30, 1297\u20131305 (2020)","journal-title":"Eur. Radiol."},{"key":"8_CR10","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Liu, X., et\u00a0al.: Deep unsupervised domain adaptation: a review of recent advances and perspectives. APSIPA Trans. Sig. Inf. Process. 11(1) (2022)","DOI":"10.1561\/116.00000192"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Lucchesi, F.R., Aredes, N.D.: The cancer genome atlas cervical squamous cell carcinoma and endocervical adenocarcinoma collection (TCGA-CESC) (2016). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.SQ4M8YP4. https:\/\/www.cancerimagingarchive.net\/collection\/tcga-cesc\/","DOI":"10.7937\/K9\/TCIA.2016.SQ4M8YP4"},{"issue":"2","key":"8_CR13","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13470","volume":"23","author":"CY Ma","year":"2022","unstructured":"Ma, C.Y., et al.: Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer. J. Appl. Clin. Med. Phys. 23(2), e13470 (2022)","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"8_CR14","unstructured":"Mortenson, M.E.: Mathematics for Computer Graphics Applications. G - Reference, Information and Interdisciplinary Subjects Series. Industrial Press (1999). https:\/\/books.google.com.hk\/books?id=YmQy799flPkC"},{"issue":"4","key":"8_CR15","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1109\/TMI.2022.3224067","volume":"42","author":"C Ouyang","year":"2022","unstructured":"Ouyang, C., et al.: Causality-inspired single-source domain generalization for medical image segmentation. IEEE Trans. Med. Imaging 42(4), 1095\u20131106 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/978-3-030-58542-6_5","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Seo","year":"2020","unstructured":"Seo, S., Suh, Y., Kim, D., Kim, G., Han, J., Han, B.: Learning to optimize domain specific normalization for domain generalization. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 68\u201383. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58542-6_5"},{"issue":"2","key":"8_CR17","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s00247-021-05042-7","volume":"52","author":"SD Serai","year":"2022","unstructured":"Serai, S.D.: Basics of magnetic resonance imaging and quantitative parameters T1, T2, T2*, T1rho and diffusion-weighted imaging. Pediatr. Radiol. 52(2), 217\u2013227 (2022)","journal-title":"Pediatr. Radiol."},{"issue":"20","key":"8_CR18","doi-asserted-by":"publisher","first-page":"5105","DOI":"10.3390\/cancers15205105","volume":"15","author":"A Shakur","year":"2023","unstructured":"Shakur, A., Lee, J.Y.J., Freeman, S.: An update on the role of MRI in treatment stratification of patients with cervical cancer. Cancers 15(20), 5105 (2023)","journal-title":"Cancers"},{"issue":"1","key":"8_CR19","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21763","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel, R.L., Miller, K.D., Wagle, N.S., Jemal, A., et al.: Cancer statistics, 2023. CA Cancer J. Clin. 73(1), 17\u201348 (2023)","journal-title":"CA Cancer J. Clin."},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Su, Z., Yao, K., Yang, X., Huang, K., Wang, Q., Sun, J.: Rethinking data augmentation for single-source domain generalization in medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 2366\u20132374 (2023)","DOI":"10.1609\/aaai.v37i2.25332"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Generalizing to unseen domains: a survey on domain generalization. IEEE Trans. Knowl. Data Eng. (2022)","DOI":"10.1109\/TKDE.2022.3178128"},{"issue":"5","key":"8_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3400066","volume":"11","author":"G Wilson","year":"2020","unstructured":"Wilson, G., Cook, D.J.: A survey of unsupervised deep domain adaptation. ACM Trans. Intell. Syst. Technol. (TIST) 11(5), 1\u201346 (2020)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"8_CR23","doi-asserted-by":"publisher","unstructured":"Xia, C., et al.: Cancer statistics in china and united states, 2022: profiles, trends, and determinants. Chin. Med. J. 135(05), 584\u2013590 (2022). https:\/\/doi.org\/10.1097\/CM9.0000000000002108","DOI":"10.1097\/CM9.0000000000002108"},{"key":"8_CR24","doi-asserted-by":"publisher","unstructured":"Xu, Y., Xie, S., Reynolds, M., Ragoza, M., Gong, M., Batmanghelich, K.: Adversarial consistency for single domain generalization in medical image segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13437, pp. 671\u2013681. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_64","DOI":"10.1007\/978-3-031-16449-1_64"},{"key":"8_CR25","unstructured":"Yoon, J.S., Oh, K., Shin, Y., Mazurowski, M.A., Suk, H.I.: Domain generalization for medical image analysis: a survey. arXiv preprint arXiv:2310.08598 (2023)"},{"issue":"4","key":"8_CR26","first-page":"4396","volume":"45","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Liu, Z., Qiao, Y., Xiang, T., Loy, C.C.: Domain generalization: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4396\u20134415 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR27","unstructured":"Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with MixStyle. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=6xHJ37MVxxp"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Qi, L., Yang, X., Ni, D., Shi, Y.: Generalizable cross-modality medical image segmentation via style augmentation and dual normalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20856\u201320865 (2022)","DOI":"10.1109\/CVPR52688.2022.02019"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, L., Ji, D., Zhu, S., Gan, W., Wu, W., Yan, J.: Learning statistical texture for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12537\u201312546 (2021)","DOI":"10.1109\/CVPR46437.2021.01235"}],"container-title":["Lecture Notes in Computer Science","Computational Mathematics Modeling in Cancer Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73360-4_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T15:03:13Z","timestamp":1728054193000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73360-4_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,5]]},"ISBN":["9783031733598","9783031733604"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73360-4_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,5]]},"assertion":[{"value":"5 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"CMMCA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Mathematics Modeling in Cancer Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cmmca2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cmmcaworkshop.github.io\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}