{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:53:10Z","timestamp":1781650390552,"version":"3.54.5"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":52,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The High-level Talents Special Support Program of Zhejiang Province","award":["2024-KYY-GXJS-0026"],"award-info":[{"award-number":["2024-KYY-GXJS-0026"]}]},{"name":"National Key Research and Development Program of China","award":["2023YFC3504600"],"award-info":[{"award-number":["2023YFC3504600"]}]},{"name":"Transverse Research Project of Zhejiang University","award":["2023-KYY-A070350007"],"award-info":[{"award-number":["2023-KYY-A070350007"]}]},{"name":"National Natural Science Foundation of Zhejiang Province","award":["LY20H180004"],"award-info":[{"award-number":["LY20H180004"]}]},{"name":"The Zhejiang Province Traditional Chinese Medicine Science and Technology Project","award":["GZY-ZJKJ-24076"],"award-info":[{"award-number":["GZY-ZJKJ-24076"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Ovarian cancer refers to a malignant tumor that grows in the ovary, which has the highest mortality rate of gynecological cancers. Positron emission tomography and computer tomography (PET\/CT) imaging is widely used for the localization and characterization of ovarian tumors, but its analysis is susceptible to the subjectivity of clinicians. A deep learning-based PET\/CT image diagnosis approach was investigated to achieve the segmentation and classification of ovarian tumors. We proposed several traditional convolutional neural networks (CNNs) for automatic ovarian tumors segmentation and classification. For segmentation, we design a hybrid network that integrates U-Net-MobileNetv3 with KiteNet to jointly capture global structure and lesion edge details, further enhanced by lesion-aware CarveMix augmentation and Dice-CE loss. For classification, we adopt ConvNeXt as the backbone and improve its robustness via Mixup data augmentation. Our method achieves a Dice coefficient of 0.826 and an accuracy of 0.912, outperforming all baseline models including U-Net, Deeplabv3, DenseNet, and Swin-Transformer. 1228 PET\/CT images were employed to train and evaluate the CNN approach. Our segmentation model obtained a Dice of 0.826 compared to 0.773 obtained by U-Net-MobileNetv3, 0.444 by KiteNet, 0.634 by FCN, 0.744 by Deeplabv3, 0.667 by U-Net, and 0.747 by U-Net-VGG. Our classification model achieved the ACC of 0.912 compared to 0.907 achieved by ConvNeXt, 0.898 by DenseNet, 0.895 by EfficientNet and 0.848 by Swin-Transformer. In this study, we developed a novel deep learning framework for the simultaneous segmentation and classification of ovarian tumors in PET\/CT imaging. Our key findings are: (1) A hybrid segmentation architecture that integrates U-Net-MobileNetv3 with KiteNet, enhanced by lesion-aware CarveMix augmentation and Dice-CE loss, achieved a Dice coefficient of 0.826, significantly outperforming standard models such as U-Net (0.667) and Deeplabv3 (0.744); (2) A classification pipeline based on ConvNeXt with Mixup data augmentation attained an accuracy of 0.912, surpassing DenseNet (0.898) and Swin-Transformer (0.848). These results demonstrate that our method can effectively support clinicians in both precise tumor delineation and reliable benign\/malignant differentiation, offering a promising tool for intelligent ovarian cancer diagnosis.<\/jats:p>","DOI":"10.1038\/s41598-026-47682-5","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T13:53:02Z","timestamp":1776174782000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent diagnosis of ovarian cancer in PET\/CT imaging based on KiteNet-MobileNetv3 fusion and CarveMix augmentation"],"prefix":"10.1038","volume":"16","author":[{"given":"Junwei","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueru","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sisheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiyun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiwen","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yating","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianmin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dexing","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,14]]},"reference":[{"issue":"6","key":"47682_CR1","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1016\/j.ejca.2012.12.027","volume":"49","author":"J Ferlay","year":"2013","unstructured":"Ferlay, J. et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012. Eur. J. Cancer 49(6), 1374\u20131403 (2013).","journal-title":"Eur. J. Cancer"},{"issue":"6","key":"47682_CR2","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1038\/nrc2644","volume":"9","author":"RC Bast Jr.","year":"2009","unstructured":"Bast, R. C. Jr., Hennessy, B. & Mills, G. B. The biology of ovarian cancer: New opportunities for translation. Nat. Rev. Cancer. 9(6), 415\u2013428 (2009).","journal-title":"Nat. Rev. Cancer."},{"issue":"5","key":"47682_CR3","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1080\/14737140.2017.1299575","volume":"17","author":"P Jessmon","year":"2017","unstructured":"Jessmon, P. et al. Epidemiology and treatment patterns of epithelial ovarian cancer. Expert Rev. Anticancer Ther. 17(5), 427\u2013437 (2017).","journal-title":"Expert Rev. Anticancer Ther."},{"issue":"10","key":"47682_CR4","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1002\/cncr.32801","volume":"126","author":"SJ Henley","year":"2020","unstructured":"Henley, S. J. et al. Annual report to the nation on the status of cancer, part II: Progress toward Healthy People 2020 objectives for 4 common cancers. Cancer 126(10), 2250\u20132266 (2020).","journal-title":"Cancer"},{"issue":"2","key":"47682_CR5","first-page":"188503","volume":"1875","author":"M Zhang","year":"2021","unstructured":"Zhang, M. et al. Roles of CA125 in diagnosis, prediction, and oncogenesis of ovarian cancer. Biochim. Biophys. Acta. 1875(2), 188503 (2021).","journal-title":"Biochim. Biophys. Acta."},{"issue":"9951","key":"47682_CR6","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1016\/S0140-6736(13)62146-7","volume":"384","author":"GC Jayson","year":"2014","unstructured":"Jayson, G. C. et al. Ovarian cancer. Lancet 384(9951), 1376\u20131388 (2014).","journal-title":"Lancet"},{"issue":"5","key":"47682_CR7","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1097\/RLU.0000000000000768","volume":"40","author":"S Schmidt","year":"2015","unstructured":"Schmidt, S. et al. Peritoneal carcinomatosis in primary ovarian cancer staging: Comparison between MDCT, MRI, and 18F-FDG PET\/CT. Clin. Nucl. Med. 40(5), 371\u2013377 (2015).","journal-title":"Clin. Nucl. Med."},{"issue":"6","key":"47682_CR8","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1007\/s00259-017-3638-z","volume":"44","author":"B Khiewvan","year":"2017","unstructured":"Khiewvan, B. et al. An update on the role of PET\/CT and PET\/MRI in ovarian cancer. Eur. J. Nucl. Med. Mol. Imaging. 44(6), 1079\u20131091 (2017).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging."},{"issue":"10","key":"47682_CR9","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1097\/RLU.0000000000002227","volume":"43","author":"D Rubello","year":"2018","unstructured":"Rubello, D., Marzola, M. C. & Colletti, P. M. The prognostic value of 18F-FDG PET\/CT in monitoring chemotherapy in ovarian cancer both at initial diagnosis and at recurrent disease. Clin. Nucl. Med. 43(10), 735\u2013738 (2018).","journal-title":"Clin. Nucl. Med."},{"issue":"3","key":"47682_CR10","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1148\/radiol.2018180547","volume":"290","author":"S Soffer","year":"2019","unstructured":"Soffer, S. et al. Convolutional neural networks for radiologic images: A radiologist\u2019s guide. Radiology 290(3), 590\u2013606 (2019).","journal-title":"Radiology"},{"key":"47682_CR11","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 61, 85\u2013117 (2015).","journal-title":"Neural Netw."},{"issue":"12","key":"47682_CR12","doi-asserted-by":"publisher","first-page":"2018","DOI":"10.1016\/j.ultrasmedbio.2010.08.019","volume":"36","author":"SJ Chen","year":"2010","unstructured":"Chen, S. J. et al. Classification of the thyroid nodules based on characteristic sonographic textural feature and correlated histopathology using hierarchical support vector machines. Ultrasoun. Med. Biol. 36(12), 2018\u20132026 (2010).","journal-title":"Ultrasoun. Med. Biol."},{"issue":"2","key":"47682_CR13","doi-asserted-by":"publisher","first-page":"91","DOI":"10.4015\/S1016237210001815","volume":"22","author":"CY Chang","year":"2010","unstructured":"Chang, C. Y. et al. Computer-aided diagnosis for thyroid Graves\u2019 disease in ultrasound images. Biomed. Eng-App. Bas. C. 22(2), 91\u201399 (2010).","journal-title":"Biomed. Eng-App. Bas. C."},{"issue":"3-4","key":"47682_CR14","first-page":"167","volume":"18","author":"S Katsigiannis","year":"2010","unstructured":"Katsigiannis, S., Keramidas, E. G. & Maroulis, D. A contourlet transform feature extraction scheme for ultrasound thyroid texture classification. IEEE Intell. Syst. 18(3\u20134), 167\u2013177 (2010).","journal-title":"IEEE Intell. Syst."},{"issue":"3","key":"47682_CR15","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1177\/0954411912472422","volume":"227","author":"UR Acharya","year":"2013","unstructured":"Acharya, U. R. et al. Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasoun. Proc. Inst. Mech. Eng. H J. Eng. Med. 227(3), 284\u2013292 (2013).","journal-title":"Proc. Inst. Mech. Eng. H J. Eng. Med."},{"key":"47682_CR16","doi-asserted-by":"crossref","unstructured":"Iakovidis D. K., Keramidas, E. G. & Maroulis, D. Fuzzy Local Binary Patterns for Ultrasound Texture Characterization 750\u2013775 (Springer-Verlag, 2008).","DOI":"10.1007\/978-3-540-69812-8_74"},{"issue":"10","key":"47682_CR17","doi-asserted-by":"publisher","first-page":"3494","DOI":"10.1016\/j.patcog.2010.04.023","volume":"43","author":"CY Chang","year":"2010","unstructured":"Chang, C. Y., Chen, S. J. & Tsai, M. F. Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images. Pattern Recognit. 43(10), 3494\u20133506 (2010).","journal-title":"Pattern Recognit."},{"issue":"6","key":"47682_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JBO.23.6.066002","volume":"23","author":"MJ Huttunen","year":"2018","unstructured":"Huttunen, M. J. et al. Automated classification of multiphoton microscopy images of ovarian tissue using deep learning. J. Biomed. Opt. 23(6), 1\u20137 (2018).","journal-title":"J. Biomed. Opt."},{"issue":"1","key":"47682_CR19","doi-asserted-by":"publisher","first-page":"2770","DOI":"10.1038\/s41598-023-29814-3","volume":"13","author":"Y Wang","year":"2023","unstructured":"Wang, Y. et al. Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging. Sci. Rep. 13(1), 2770 (2023).","journal-title":"Sci. Rep."},{"issue":"6","key":"47682_CR20","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/S0010-4825(00)00015-9","volume":"30","author":"P Dastidar","year":"2000","unstructured":"Dastidar, P. et al. Magnetic resonance imaging based volume estimation of ovarian tumours: Use of a segmentation and 3D reformation software. Comput. Biol. Med. 30(6), 329\u2013340 (2000).","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"47682_CR21","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.21037\/qims-22-494","volume":"13","author":"D Hu","year":"2023","unstructured":"Hu, D. et al. Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images. Quant. Imaging Med. Surg. 13(3), 1464\u20131477 (2023).","journal-title":"Quant. Imaging Med. Surg."},{"key":"47682_CR22","unstructured":"Tan, M. & Le, Q. V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv e-prints. 6105\u20136114 (2019)."},{"key":"47682_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows. arXiv e-prints. 10012\u201310022 (2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"47682_CR24","unstructured":"Huang, G., Liu, Z., Laurens, V., et al. Densely connected convolutional networks. arXiv e-prints. 4700\u20134708 (2017)."},{"key":"47682_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C. Y., et al. A ConvNet for the 2020s. arXiv e-prints. 11976\u201311986 (2022).","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"47682_CR26","unstructured":"Zhang, H., Cisse, M., Dauphin, Y. N, et al. mixup: Beyond empirical risk minimization. arXiv preprint arXiv. 1710.09412 (2017)."},{"issue":"3","key":"47682_CR27","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1001\/jamaophthalmol.2019.5710","volume":"138","author":"M Mazloumi","year":"2020","unstructured":"Mazloumi, M. et al. Accuracy of the Cancer Genome Atlas classification versus American Joint Committee on Cancer classification for prediction of metastasis in patients with uveal melanoma. JAMA Ophthalmol. 138(3), 260\u2013267 (2020).","journal-title":"JAMA Ophthalmol."},{"issue":"4","key":"47682_CR28","first-page":"640","volume":"39","author":"J Long","year":"2015","unstructured":"Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2015).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"47682_CR29","doi-asserted-by":"crossref","unstructured":"Chen, L. C, Zhu. Y, Papandreou, G., et al. Encoder-decoder with atrous separable convolution for semantic image segmentation. ECCV. 801\u2013818 (2018).","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"47682_CR30","doi-asserted-by":"publisher","first-page":"120041","DOI":"10.1016\/j.neuroimage.2023.120041","volume":"271","author":"X Zhang","year":"2023","unstructured":"Zhang, X. et al. CarveMix: A simple data augmentation method for brain lesion segmentation. Neuroimage 271, 120041 (2023).","journal-title":"Neuroimage"},{"issue":"15","key":"47682_CR31","doi-asserted-by":"publisher","first-page":"5676","DOI":"10.3390\/s22155676","volume":"22","author":"J Deng","year":"2022","unstructured":"Deng, J. et al. Assessing macro disease index of wheat stripe rust based on Segformer with complex background in the field. Sensors 22(15), 5676 (2022).","journal-title":"Sensors"},{"issue":"4","key":"47682_CR32","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/TMI.2021.3130469","volume":"41","author":"JMJ Valanarasu","year":"2022","unstructured":"Valanarasu, J. M. J. et al. KiU-Net: Overcomplete convolutional architectures for biomedical image and volumetric segmentation. IEEE Trans. Med. Imaging 41(4), 965\u2013976 (2022).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"47682_CR33","doi-asserted-by":"publisher","first-page":"1007453","DOI":"10.3389\/fnins.2022.1007453","volume":"16","author":"BD Basaran","year":"2022","unstructured":"Basaran, B. D., Matthews, P. M. & Bai, W. New lesion segmentation for multiple sclerosis brain images with imaging and lesion-aware augmentation. Front. Neurosci. 16, 1007453 (2022).","journal-title":"Front. Neurosci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47682-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47682-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47682-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:02:44Z","timestamp":1781647364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47682-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,14]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["47682"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-47682-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,14]]},"assertion":[{"value":"9 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2026","order":3,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"We confirmed that we have read the Editorial Policy pages. This study was conducted with approval from the Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University on February 8, 2022. (No.2022-KL-008-01). This study was conducted in accordance with the declaration of Helsinki. This study has approved to waive the informed consent from all patients, because in this study, PET\/CT images were obtained from PET\/CT workstations, all of which were images of ovarian cancer patients who had undergone PET\/CT examination. This study only analyzed the imaging features and texture of PET\/CT images of ovarian cancer patients, and did not involve patient identity information. The images were limited to this study and were not used for other purposes. All PET\/CT patients sign informed consent prior to the examination, giving consent for the images to be used for scientific research, as mentioned in the attached files.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"17456"}}