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detection-tracking tasks are vital for biomedical image analysis with potential applications in clinical diagnosis and treatment. However, it poses challenges such as ambiguous boundaries and complex backgrounds in microscopic video sequences, leading to missed detection, false detection, and loss of tracking. Therefore, we propose an enhanced multiple object tracking algorithm IEGS-YOLO + BoT-SORT, named IEGS-BoT, to address these issues. Firstly, the IEGS-YOLO detector is developed for cell detection tasks. It uses the iEMA module, which effectively combines the global information to enhance the local information. Then, we replace the traditional convolutional network in the neck of the YOLO11n with GSConv to reduce the computational complexity while maintaining accuracy. Finally, the BoT-SORT tracker is selected to enhance the accuracy of bounding box positioning through camera motion compensation and Kalman filter. We conduct experiments on the CTMC dataset, and the results show that in the detection phase, the map50 (mean Average Precision) and map50\u201395 values are 73.2% and 32.6%, outperforming the YOLO11n detector by 1.1% and 0.6%, respectively. In the tracking phase, using the IEGS-BoT method, the multiple objects tracking accuracy (MOTA), higher order tracking accuracy (HOTA), and identification F1 (IDF1) reach 53.97%, 51.30%, and 67.52%, respectively. Compared with the base BoT-SORT, the proposed method achieves improvements of 1.19%, 0.23%, and 1.29% in MOTA, HOTA, and IDF1, respectively. ID switch (IDSW) decreases from 1170 to 894, which demonstrates significant mitigation of identity confusion. This approach effectively addresses the challenges posed by object loss and identity switching in cell tracking, providing a more reliable solution for medical image analysis.<\/jats:p>","DOI":"10.3390\/biomimetics10090564","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:09:53Z","timestamp":1756080593000},"page":"564","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["IEGS-BoT: An Integrated Detection-Tracking Framework for Cellular Dynamics Analysis in Medical Imaging"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5844-2360","authenticated-orcid":false,"given":"Shuqin","family":"Tu","sequence":"first","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidian","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Mao","sequence":"additional","affiliation":[{"name":"Undergraduate School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaying","family":"Du","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1038\/s41592-023-01879-y","article-title":"The Cell Tracking Challenge: 10 years of objective benchmarking","volume":"20","author":"Maska","year":"2023","journal-title":"Nat. Methods"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.visinf.2020.11.003","article-title":"Computerized cell tracking: Current methods, tools and challenges","volume":"5","author":"Emami","year":"2021","journal-title":"Vis. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ceb.2018.12.013","article-title":"Non-canonical processes that shape the cell migration landscape","volume":"57","author":"Majumdar","year":"2019","journal-title":"Curr. Opin. Cell Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114095","DOI":"10.1016\/j.eswa.2020.114095","article-title":"Medical image based breast cancer diagnosis: State of the art and future directions","volume":"167","author":"Tariq","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zheng, D., He, X., and Jing, J. (2023). Overview of Artificial Intelligence in Breast Cancer Medical Imaging. J. Clin. Med., 12.","DOI":"10.3390\/jcm12020419"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-Net: Deep learning for cell counting, detection, and morphometry","volume":"16","author":"Falk","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2432","DOI":"10.1109\/TMI.2022.3163171","article-title":"A Task Decomposing and Cell Comparing Method for Cervical Lesion Cell Detection","volume":"41","author":"Chen","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"67987","DOI":"10.1109\/ACCESS.2023.3290905","article-title":"Blood Cell Detection Method Based on Improved, YOLO.v5","volume":"11","author":"Guo","year":"2023","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1111\/cyt.13235","article-title":"Relationship between a deep learning model and liquid-based cytological processing techniques","volume":"34","author":"Ikeda","year":"2023","journal-title":"Cytopathology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"125139","DOI":"10.1016\/j.eswa.2024.125139","article-title":"DB-FCN: An end-to-end dual-branch fully convolutional nucleus detection model","volume":"257","author":"Xie","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sazak, H., and Kotan, M. (2024). Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights. Diagnostics, 15.","DOI":"10.3390\/diagnostics15010022"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107392","DOI":"10.1016\/j.engappai.2023.107392","article-title":"Modified YOLO network model for metaphase cell detection in antinuclear antibody images","volume":"127","author":"Chang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, X., Zheng, H., Tang, H., and Li, F. (2024). Multi-scale perceptual YOLO for automatic detection of clue cells and trichomonas in fluorescence microscopic images. Comput. Biol. Med., 175.","DOI":"10.1016\/j.compbiomed.2024.108500"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tarimo, S.A., Jang, M.A., Ngasa, E.E., Shin, H.B., Shin, H., and Woo, J. (2024). WBC YOLO-ViT: 2 Way-2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer. Comput. Biol. Med., 169.","DOI":"10.1016\/j.compbiomed.2023.107875"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e24403","DOI":"10.1016\/j.heliyon.2024.e24403","article-title":"YOLO and residual network for colorectal cancer cell detection and counting","volume":"10","author":"Haq","year":"2024","journal-title":"Heliyon"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liao, W., He, Y., Jiang, B., Zhao, J., Gao, M., and Zhang, X. (2025). HistoMoCo: Momentum Contrastive Learning Pre-Training on Unlabeled Histopathological Images for Oral Squamous Cell Carcinoma Detection. Electronics, 14.","DOI":"10.3390\/electronics14071252"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Scherr, T., Loffler, K., Bohland, M., and Mikut, R. (2020). Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0243219"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s00138-020-01072-7","article-title":"Coupling cell detection and tracking by temporal feedback","volume":"31","author":"Sixta","year":"2020","journal-title":"Mach. Vis. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, J., Su, X., Zhao, L., and Zhang, J. (2020). Deep Reinforcement Learning for Data Association in Cell Tracking. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00298"},{"key":"ref_21","unstructured":"Tal, A., Ismail Ben, A., Marleen de, B., Maxime, D., Herve, L., and Christopher, P. (2020, January 13\u201318). Siamese Tracking of Cell Behaviour Patterns. Proceedings of the Third Conference on Medical Imaging with Deep Learning, Vienna, Austria."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, Y., Song, Y., Zhang, C., Zhang, F., O\u2019Donnell, L., Chrzanowski, W., and Cai, W. (2021, January 13\u201316). Celltrack R-CNN: A Novel End-to-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images. Proceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France.","DOI":"10.1109\/ISBI48211.2021.9434057"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ben-Haim, T., and Raviv, T.R. (2022, January 23\u201327). Graph Neural Network for Cell Tracking in Microscopy Videos. Proceedings of the Computer Vision\u2014ECCV 2022: 17th European Conference, Tel Aviv, Israel. Part XXI.","DOI":"10.1007\/978-3-031-19803-8_36"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wu, D., Huang, Z., and Zhang, Y. (2025). SolidTrack: A Novel Method for Robust and Reliable Multi-Pedestrian Tracking. Electronics, 14.","DOI":"10.3390\/electronics14071370"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., and Wei, Y. (2022, January 23\u201327). MOTR: End-to-End Multiple-Object Tracking with Transformer. Proceedings of the Computer Vision\u2014ECCV 2022: 17th European Conference, Tel Aviv, Israel. Part XXVII.","DOI":"10.1007\/978-3-031-19812-0_38"},{"key":"ref_26","unstructured":"Anjum, S., and Gurari, D. (2023, January 17\u201324). CTMC: Cell Tracking with Mitosis Detection Dataset Challenge. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Vancouver, BC, Canada."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1109\/OJEMB.2023.3288470","article-title":"Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage","volume":"5","author":"Toubal","year":"2024","journal-title":"IEEE Open J. Eng. Med. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., and Huang, Z. (2023, January 4\u20139). Efficient multi-scale attention module with cross-spatial learning. Proceedings of the ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, X., Li, J., Liu, L., Xue, Z., Zhang, B., Jiang, Z., Huang, T., Wang, Y., and Wang, C. (2023, January 2\u20136). Rethinking mobile block for efficient attention-based models. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00134"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s11554-024-01436-6","article-title":"Slim-neck by GSConv: A lightweight-design for real-time detector architectures","volume":"21","author":"Li","year":"2024","journal-title":"J. Real-Time Image Process."},{"key":"ref_31","unstructured":"Aharon, N., Orfaig, R., and Bobrovsky, B.-Z. (2022). Bot-Sort: Robust Associations Multi-Pedestrian Tracking. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tu, S., Liu, H., Mao, L., Tu, C., Ye, W., Yu, H., and Chen, W. (2024). The urine formed element instance segmentation based on YOLOv5n. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-79969-w"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106442","DOI":"10.1016\/j.engappai.2023.106442","article-title":"Mixed local channel attention for object detection","volume":"123","author":"Wan","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8906","DOI":"10.1109\/TMM.2023.3243616","article-title":"Dilateformer: Multi-scale dilated transformer for visual recognition","volume":"25","author":"Jiao","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, Y., Wang, G., and Liu, X. (2024, January 17\u201321). Multi-scale attention network for single image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPRW63382.2024.00602"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.H., He, H., Zhuo, W., Wen, S., Lee, C.H., and Chan, S.H.G. (2023, January 17\u201324). Run, don\u2019t walk: Chasing higher FLOPS for faster neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"ref_37","unstructured":"Wang, C.Y., Yeh, I.H., and Mark Liao, H.Y. (October, January 29). Yolov9: Learning what you want to learn using programmable gradient information. Proceedings of the European Conference on Computer Vision, Milan, Italy."},{"key":"ref_38","first-page":"107984","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","unstructured":"Sun, P., Cao, J., Jiang, Y., Zhang, R., Xie, E., Yuan, Z., Wang, C., and Luo, P. (2020). Transtrack: Multiple Object Tracking with Transformer. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple online and realtime tracking with a deep association metric. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Stadler, D., and Beyerer, J. (2023, January 17\u201324). An improved association pipeline for multi-person tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00319"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cao, J., Pang, J., Weng, X., Khirodkar, R., and Kitani, K. (2023, January 17\u201324). Observation-centric sort: Rethinking sort for robust multi-object tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Maggiolino, G., Ahmad, A., Cao, J., and Kitani, K. (2023, January 17\u201324). Deep oc-sort: Multi-pedestrian tracking by adaptive re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/ICIP49359.2023.10222576"},{"key":"ref_44","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (October, January 29). Bytetrack: Multi-object tracking by associating every detection box. Proceedings of the European Conference on Computer Vision, Milan, Italy."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/9\/564\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:35:18Z","timestamp":1760034918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/9\/564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,24]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["biomimetics10090564"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics10090564","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,24]]}}}