{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T02:18:26Z","timestamp":1773368306475,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shandong Provincial","award":["ZR2024MF037"],"award-info":[{"award-number":["ZR2024MF037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In-depth research on marine biodiversity is essential for understanding and protecting marine ecosystems, where semantic segmentation of marine species plays a crucial role. However, segmenting microscopic zooplankton images remains challenging due to highly variable morphologies, complex boundaries, and the scarcity of high-quality pixel-level annotations that require expert knowledge. Existing semi-supervised methods often rely on single-model perspectives, producing unreliable pseudo-labels and limiting performance in such complex scenarios. To address these challenges, this paper proposes a consistency-driven dual-teacher framework tailored for zooplankton segmentation. Two heterogeneous teacher networks are employed: one captures global morphological features, while the other focuses on local fine-grained details, providing complementary and diverse supervision and alleviating overfitting under limited annotations. In addition, a dynamic fusion-based pseudo-label filtering strategy is introduced to adaptively integrate hard and soft labels by jointly considering prediction consistency and confidence scores, thereby enhancing supervision flexibility. Extensive experiments on the Zooplankton-21 Microscopic Segmentation Dataset (ZMS-21), a self-constructed microscopic zooplankton dataset demonstrate that the proposed method consistently outperforms existing semi-supervised segmentation approaches under various annotation ratios, achieving mIoU scores of 64.80%, 69.58%, 70.32%, and 73.92% with 1\/16, 1\/8, 1\/4, and 1\/2 labeled data, respectively.<\/jats:p>","DOI":"10.3390\/jimaging12030125","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T13:12:24Z","timestamp":1773321144000},"page":"125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Consistency-Driven Dual-Teacher Framework for Semi-Supervised Zooplankton Microscopic Image Segmentation"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhongwei","family":"Li","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4435-2740","authenticated-orcid":false,"given":"Yinglin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dekun","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanping","family":"Qi","sequence":"additional","affiliation":[{"name":"North China Sea Environmental Monitoring Center, State Oceanic Administration, Qingdao 266033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Song","sequence":"additional","affiliation":[{"name":"North China Sea Environmental Monitoring Center, State Oceanic Administration, Qingdao 266033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Caron, M., Bojanowski, P., Joulin, A., and Douze, M. (2018, January 8\u201314). Deep clustering for unsupervised learning of visual features. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref_2","unstructured":"Ji, X., Henriques, J.F., and Vedaldi, A. (November, January 27). Invariant information clustering for unsupervised image classification and segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_3","unstructured":"Hamilton, M., Zhang, Z., Hariharan, B., Snavely, N., and Freeman, W.T. (2022). Unsupervised semantic segmentation by distilling feature correspondences. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., and Joulin, A. (2021, January 10\u201317). Emerging properties in self-supervised vision transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_6","unstructured":"Hoffman, J., Wang, D., Yu, F., and Darrell, T. (2016). FCNs in the wild: Pixel-level adversarial and constraint-based adaptation. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker, M. (2018, January 18\u201323). Learning to adapt structured output space for semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00780"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, J., Liu, Y., Cheng, Y., and Qi, Y. (2024). SemiSAM: Enhancing semi-supervised medical image segmentation via SAM-assisted consistency regularization. Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.","DOI":"10.1109\/BIBM62325.2024.10821951"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2948","DOI":"10.1109\/TMI.2025.3556310","article-title":"Segment together: A versatile paradigm for semi-supervised medical image segmentation","volume":"44","author":"Zeng","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","unstructured":"Ma, W., Karakus, O., and Rosin, P.L. (2023). DiverseNet: Decision diversified semi-supervised semantic segmentation networks for remote sensing imagery. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, Q., Gu, X., Henderson, P., and Deligianni, F. (2023). Multi-scale cross contrastive learning for semi-supervised medical image segmentation. arXiv.","DOI":"10.36227\/techrxiv.172469505.55267498\/v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Xu, H., Zhang, W., Gao, B., and Heng, P.-A. (2021, January 10\u201317). C3-SemiSeg: Contrastive semi-supervised segmentation via cross-set learning and dynamic class-balancing. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00695"},{"key":"ref_13","unstructured":"Liu, S., Zhi, S., Johns, E., and Davison, A.J. (2021). Bootstrapping semantic segmentation with regional contrast. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, H., Shen, Y., Fei, J., Li, W., Jin, G., Wu, L., Zhao, R., and Le, X. (2022, January 18\u201324). Semi-supervised semantic segmentation using unreliable pseudo-labels. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00421"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high-resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., and Wang, Y. (2020). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. arXiv.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., and Girdhar, R. (2022, January 18\u201324). Masked-attention mask transformer for universal image segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102792","DOI":"10.1016\/j.media.2023.102792","article-title":"Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation","volume":"87","author":"Chaitanya","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_21","unstructured":"Lee, D.H. (2013, January 20\u201321). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. Proceedings of the Workshop on Challenges in Representation Learning, ICML, Atlanta, GA, USA. Available online: https:\/\/www.researchgate.net\/publication\/280581078."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108925","DOI":"10.1016\/j.patcog.2022.108925","article-title":"Learning pseudo labels for semi- and weakly supervised semantic segmentation","volume":"132","author":"Wang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"French, G., Laine, S., Aila, T., Mackiewicz, M., and Finlayson, G. (2019). Semi-supervised semantic segmentation needs strong, varied perturbations. arXiv.","DOI":"10.5244\/C.34.154"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Y., Tian, Y., Chen, Y., Liu, F., Belagiannis, V., and Carneiro, G. (2022, January 18\u201324). Perturbed and strict mean teachers for semi-supervised semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Yang, L., Long, S., Pi, J., Zhou, L., and Wang, J. (2023, January 17\u201324). Augmentation matters: A simple-yet-effective approach to semi-supervised semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01092"},{"key":"ref_26","unstructured":"Rizve, M.N., Duarte, K., Rawat, Y.S., and Shah, M. (2021). In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning. arXiv."},{"key":"ref_27","first-page":"5851","article-title":"Cross semi-supervised semantic segmentation network based on differential feature extraction","volume":"36","author":"Chen","year":"2025","journal-title":"J. Softw."},{"key":"ref_28","first-page":"330","article-title":"Research on semi-supervised learning algorithm based on improved self-training","volume":"35","author":"Yao","year":"2025","journal-title":"High Technol. Lett."},{"key":"ref_29","unstructured":"Zou, Y., Zhang, Z., Zhang, H., Li, C.-L., Bian, X., Huang, J.-B., and Pfister, T. (2020). PseudoSeg: Designing pseudo labels for semantic segmentation. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhuo, W., Qi, L., Shi, Y., and Gao, Y. (2022, January 18\u201324). ST++: Make self-training work better for semi-supervised semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"ref_31","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, L., Qi, L., Feng, L., Zhang, W., and Shi, Y. (2023, January 17\u201324). Revisiting weak-to-strong consistency in semi-supervised semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"591","DOI":"10.37188\/OPE.20253304.0591","article-title":"Pseudo-label confidence regulates semi-supervised semantic segmentation of pathological images of colorectal cancer","volume":"33","author":"Xu","year":"2025","journal-title":"Opt. Precis. Eng."},{"key":"ref_34","unstructured":"Tarvainen, A., and Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_35","first-page":"22106","article-title":"Semi-supervised semantic segmentation via adaptive equalization learning","volume":"34","author":"Hu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., and Wang, J. (2021, January 20\u201325). Semi-supervised semantic segmentation with cross pseudo supervision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2803","DOI":"10.52202\/068431-0203","article-title":"Semi-supervised semantic segmentation via gentle teaching assistant","volume":"35","author":"Jin","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, X., Bai, H., Yu, L., Zhao, Y., and Xiao, J. (2024, January 16\u201322). Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00318"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"48639","DOI":"10.52202\/075280-2111","article-title":"Dual mean-teacher: An unbiased semi-supervised framework for audio-visual source localization","volume":"36","author":"Guo","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wu, T., Ding, C., Zhao, H., and Guo, G. (2022). Region-level contrastive and consistency learning for semi-supervised semantic segmentation. arXiv.","DOI":"10.24963\/ijcai.2022\/226"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Qin, J., Wu, J., Li, M., Xiao, X., Zheng, M., and Wang, X. (2022). Multi-granularity distillation scheme towards lightweight semi-supervised semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-031-20056-4_28"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jeffries, H.P., Sherman, K., Maurer, R., and Katsinis, C. (1980). Computer-processing of zooplankton samples. Estuarine Perspectives, Academic Press.","DOI":"10.1016\/B978-0-12-404060-1.50033-2"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/BF00393019","article-title":"Automated sizing, counting and identification of zooplankton by pattern recognition","volume":"78","author":"Jeffries","year":"1984","journal-title":"Mar. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Thonnat, M., and Gandelin, M. (1988). An expert system for the automatic classification and description of zooplanktons from monocular images. Proceedings of the 9th International Conference on Pattern Recognition (ICPR), IEEE.","DOI":"10.1109\/ICPR.1988.28185"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bure\u0161, J., Eerola, T., Lensu, L., K\u00e4lvi\u00e4inen, H., and Zem\u010d\u00edk, P. (2021). Plankton recognition in images with varying size. Pattern Recognition Workshops and Challenges, Springer.","DOI":"10.1007\/978-3-030-68780-9_11"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102783","DOI":"10.1016\/j.apor.2021.102783","article-title":"Toward in situ zooplankton detection with a densely connected YOLOv3 model","volume":"114","author":"Li","year":"2021","journal-title":"Appl. Ocean Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/TIP.2024.3359041","article-title":"Spatial structure constraints for weakly supervised semantic segmentation","volume":"33","author":"Chen","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5219","DOI":"10.1109\/TIP.2024.3458854","article-title":"Progressive learning with cross-window consistency for semi-supervised semantic segmentation","volume":"33","author":"Dang","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","first-page":"40367","article-title":"Switching temporary teachers for semi-supervised semantic segmentation","volume":"36","author":"Na","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4343","DOI":"10.1007\/s11263-024-02016-8","article-title":"PRCL: Probabilistic representation contrastive learning for semi-supervised semantic segmentation","volume":"132","author":"Xie","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8). Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ke, Z., Qiu, D., Li, K., Yan, Q., and Lau, R.W.H. (2020). Guided collaborative training for pixel-wise semi-supervised learning. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-58601-0_26"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/3\/125\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:34:13Z","timestamp":1773326053000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/3\/125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":54,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["jimaging12030125"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12030125","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}