{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:51:27Z","timestamp":1780462287675,"version":"3.54.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"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":["Wireless Pers Commun"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s11277-024-11379-7","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T04:03:28Z","timestamp":1719547408000},"page":"813-851","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Systematic Survey on Biological Cell Image Segmentation and Cell Counting Techniques in Microscopic Images Using Machine Learning"],"prefix":"10.1007","volume":"137","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7062-3364","authenticated-orcid":false,"given":"Harjeet","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harpreet","family":"Kaur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"11379_CR1","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.addr.2018.01.011","volume":"125","author":"S Riethdorf","year":"2018","unstructured":"Riethdorf, S., O\u2019Flaherty, L., Hille, C., & Pantel, K. (2018). Clinical applications of the Cell Search platform in cancerpatients. Advanced Drug Delivery Reviews, 125, 102\u2013121. https:\/\/doi.org\/10.1016\/j.addr.2018.01.011","journal-title":"Advanced Drug Delivery Reviews"},{"key":"11379_CR2","unstructured":"Rizwan, S. M. (2015). Automated blood cells segmentation & counting."},{"issue":"1","key":"11379_CR3","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1002\/cyto.a.20153","volume":"66","author":"B Nilsson","year":"2005","unstructured":"Nilsson, B., & Heyden, A. (2005). Segmentation of complex cell clusters in microscopic images: Application to bonemarrow samples. Cytometry Part A, 66(1), 24\u201331. https:\/\/doi.org\/10.1002\/cyto.a.20153","journal-title":"Cytometry Part A"},{"issue":"1","key":"11379_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/cyto.a.10106","volume":"57","author":"Q Zheng","year":"2004","unstructured":"Zheng, Q., Milthorpe, B. K., & Jones, A. S. (2004). Direct neural network application for automated cell recognition. Cytometry Part A, 57(1), 1\u20139. https:\/\/doi.org\/10.1002\/cyto.a.10106","journal-title":"Cytometry Part A"},{"issue":"6024","key":"11379_CR5","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1126\/science.1203543","volume":"331","author":"CL Chaffer","year":"2011","unstructured":"Chaffer, C. L., & Weinberg, R. A. (2011). A perspective on cancer cell metastasis. Science, 331(6024), 1559\u20131564. https:\/\/doi.org\/10.1126\/science.1203543","journal-title":"Science"},{"issue":"1","key":"11379_CR6","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s00500-005-0458-z","volume":"10","author":"K Jiang","year":"2006","unstructured":"Jiang, K., Liao, Q. M., & Xiong, Y. (2006). A novel white blood cell segmentation scheme based on feature spaceclustering. Soft Computing, 10(1), 12\u201319. https:\/\/doi.org\/10.1007\/s00500-005-0458-z","journal-title":"Soft Computing"},{"key":"11379_CR7","unstructured":"Wersing, H. (2002). Learning lateral interactions for feature binding and sensory segmentation. Advances in NeuralInformation Processing Systems."},{"key":"11379_CR8","doi-asserted-by":"crossref","unstructured":"Panagiotakis, C., & Argyros, A. A. (2018). CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING Institute of Computer Science , FORTH , Greece Business Administration Department ( Agios Nikolaos ), TEI of Crete , Greece Computer Science Department , University of Crete , Greece Email : cpanag , ar. In: 25th IEEE International Conference on Image Processing (ICIP), 2426\u20132430.","DOI":"10.1109\/ICIP.2018.8451852"},{"issue":"7","key":"11379_CR9","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1002\/cyto.a.23686","volume":"95","author":"Z Wang","year":"2019","unstructured":"Wang, Z. (2019). Cell segmentation for image cytometry: advances, insufficiencies, and challenges. Cytometry PartA, 95(7), 708\u2013711. https:\/\/doi.org\/10.1002\/cyto.a.23686","journal-title":"Cytometry PartA"},{"issue":"8","key":"11379_CR10","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1016\/S0730-725X(03)00185-1","volume":"21","author":"KCR Lin","year":"2003","unstructured":"Lin, K. C. R., Yang, M. S., Liu, H. C., Lirng, J. F., & Wang, P. N. (2003). Generalized Kohonen\u2019s competitive learning algorithms for ophthalmological MR image segmentation. Magnetic Resonance Imaging, 21(8), 863\u2013870. https:\/\/doi.org\/10.1016\/S0730-725X(03)00185-1","journal-title":"Magnetic Resonance Imaging"},{"issue":"2","key":"11379_CR11","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1109\/MSP.2006.1598095","volume":"23","author":"X Zhou","year":"2006","unstructured":"Zhou, X., & Wong, S. T. C. (2006). High content cellular imaging for drug development. IEEE Signal Processing Magazine, 23(2), 170\u2013174. https:\/\/doi.org\/10.1109\/MSP.2006.1598095","journal-title":"IEEE Signal Processing Magazine"},{"key":"11379_CR12","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.sna.2018.03.009","volume":"274","author":"Y Zeng","year":"2018","unstructured":"Zeng, Y., Jin, K., Li, J., Liu, J., Li, J., Li, T., & Li, S. (2018). A low cost and portable smartphone microscopic device for cell counting. Sensors and Actuators, A: Physical, 274, 57\u201363. https:\/\/doi.org\/10.1016\/j.sna.2018.03.009","journal-title":"Sensors and Actuators, A: Physical"},{"key":"11379_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101892","volume":"68","author":"S He","year":"2021","unstructured":"He, S., Minn, K. T., Solnica-Krezel, L., Anastasio, M. A., & Li, H. (2021). Deeply-supervised density regression for automatic cell counting in microscopy images. Medical Image Analysis, 68, 101892. https:\/\/doi.org\/10.1016\/j.media.2020.101892","journal-title":"Medical Image Analysis"},{"key":"11379_CR14","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.asoc.2015.12.038","volume":"46","author":"P Ghosh","year":"2016","unstructured":"Ghosh, P., Bhattacharjee, D., & Nasipuri, M. (2016). Blood smear analyzer for white blood cell counting: A hybrid microscopic image analyzing technique. Applied Soft Computing Journal, 46, 629\u2013638. https:\/\/doi.org\/10.1016\/j.asoc.2015.12.038","journal-title":"Applied Soft Computing Journal"},{"key":"11379_CR15","doi-asserted-by":"publisher","DOI":"10.5772\/23147","author":"GA MHidalgo","year":"2011","unstructured":"MHidalgo, G. A. (2011). Image processing methods for automatic cell counting in vivo or in situ using 3d confocal microscopy. Advanced Biomedical Engineering, August. https:\/\/doi.org\/10.5772\/23147","journal-title":"Advanced Biomedical Engineering, August."},{"issue":"March","key":"11379_CR16","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.ab.2014.12.007","volume":"473","author":"IV Grishagin","year":"2015","unstructured":"Grishagin, I. V. (2015). Automatic cell counting with ImageJ. Analytical Biochemistry, 473(March), 63\u201365. https:\/\/doi.org\/10.1016\/j.ab.2014.12.007","journal-title":"Analytical Biochemistry"},{"key":"11379_CR17","unstructured":"Miguel, H., & Andrade, F. De. (2015). Image Processing Methodology for Blood Cell Counting via Mobile Devices"},{"key":"11379_CR18","doi-asserted-by":"publisher","unstructured":"Guo, X., & Yu, F. (2013). A method of automatic cell counting based on microscopic image. In: Proceedings - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013, 1, 293\u2013296.https:\/\/doi.org\/10.1109\/IHMSC.2013.76","DOI":"10.1109\/IHMSC.2013.76"},{"issue":"3","key":"11379_CR19","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1080\/21681163.2016.1149104","volume":"6","author":"W Xie","year":"2018","unstructured":"Xie, W., Noble, J. A., & Zisserman, A. (2018). Microscopy cell counting and detection with fully convolutional regression networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 6(3), 283\u2013292. https:\/\/doi.org\/10.1080\/21681163.2016.1149104","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization"},{"key":"11379_CR20","doi-asserted-by":"publisher","unstructured":"Venkatalakshmi, B., & Thilagavathi, K. (2013). Automatic red blood cell counting using hough transform. 2013 IEEE Conference on Information and Communication Technologies, ICT 2013, Ict, 267\u2013271. https:\/\/doi.org\/10.1109\/CICT.2013.6558103","DOI":"10.1109\/CICT.2013.6558103"},{"key":"11379_CR21","unstructured":"Neelakantan, S., Sushanth, S., & Kalidindi, V. (2020). Master thesis master \u2019 s programme in embedded and intelligent analyzing white blood cells using deep learning techniques Computer science and engineering , 30."},{"issue":"9","key":"11379_CR22","doi-asserted-by":"publisher","first-page":"e1003812","DOI":"10.1371\/journal.pmed.1003812","volume":"18","author":"N Ford","year":"2021","unstructured":"Ford, N., Eshun-Wilson, I., Ameyan, W., Newman, M., Vojnov, L., Doherty, M., & Geng, E. (2021). Future directions for HIV service delivery research: Research gaps identified through WHO guideline development process. PLoS Medicine, 18(9), e1003812. https:\/\/doi.org\/10.1371\/journal.pmed.1003812","journal-title":"PLoS Medicine"},{"issue":"11","key":"11379_CR23","doi-asserted-by":"publisher","first-page":"4912","DOI":"10.3390\/app11114912","volume":"11","author":"F Lavitt","year":"2021","unstructured":"Lavitt, F., Rijlaarsdam, D. J., van der Linden, D., Weglarz-Tomczak, E., & Tomczak, J. M. (2021). Deep learning and transfer learning for automatic cell counting in microscope images of human cancer cell lines. Applied Sciences, 11(11), 4912. https:\/\/doi.org\/10.3390\/app11114912","journal-title":"Applied Sciences"},{"key":"11379_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphys.2018.00085","volume":"9","author":"X Zhi","year":"2018","unstructured":"Zhi, X., Wang, J., Lu, P., Jia, J., Shen, H. B., & Ning, G. (2018). AdipoCount: A new software for automatic adipocyte counting. Frontiers in Physiology, 9, 1\u20139. https:\/\/doi.org\/10.3389\/fphys.2018.00085","journal-title":"Frontiers in Physiology"},{"key":"11379_CR25","doi-asserted-by":"publisher","first-page":"81945","DOI":"10.1109\/ACCESS.2019.2920933","volume":"7","author":"RM Rad","year":"2019","unstructured":"Rad, R. M., Saeedi, P., Au, J., & Havelock, J. (2019). Cell-Net: Embryonic cell counting and centroid localization via residual incremental atrous pyramid and progressive upsampling convolution. IEEE Access, 7, 81945\u201381955. https:\/\/doi.org\/10.1109\/ACCESS.2019.2920933","journal-title":"IEEE Access"},{"issue":"1","key":"11379_CR26","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1002\/(SICI)1097-0320(19990501)36:1<18::AID-CYTO3>3.0.CO;2-J","volume":"36","author":"PJ Sj\u00f6str\u00f6m","year":"1999","unstructured":"Sj\u00f6str\u00f6m, P. J., Frydel, B. R., & Wahlberg, L. U. (1999). Artificial neural network-aided image analysis system for cell counting. Cytometry, 36(1), 18\u201326. https:\/\/doi.org\/10.1002\/(SICI)1097-0320(19990501)36:1%3c18::AID-CYTO3%3e3.0.CO;2-J","journal-title":"Cytometry"},{"key":"11379_CR27","unstructured":"Sarkar, S. (2017). NIST-FDA Cell Counting Workshop : Challenges in Cell Counting Cell Count"},{"key":"11379_CR28","unstructured":"Microscopy Res Technique-2021-Lin-Automatic cell counting for phase\u2010contrast microscopic images based on a 2022 NEW IMP.pdf. (n.d.)."},{"issue":"6","key":"11379_CR29","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1364\/osac.393971","volume":"3","author":"L Nichele","year":"2020","unstructured":"Nichele, L., Persichetti, V., Lucidi, M., & Cincotti, G. (2020). Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting. OSA Continuum, 3(6), 1417. https:\/\/doi.org\/10.1364\/osac.393971","journal-title":"OSA Continuum"},{"key":"11379_CR30","doi-asserted-by":"publisher","first-page":"3685","DOI":"10.1007\/s11277-022-09533-0","volume":"124","author":"Y Singh","year":"2022","unstructured":"Singh, Y., Kaur, L., & Neeru, N. (2022). A new improved obstacle detection framework using IDCT and CNN to assist visually impaired persons in an outdoor environment. Wireless Personal Communications, 124, 3685\u20133702.","journal-title":"Wireless Personal Communications"},{"key":"11379_CR31","doi-asserted-by":"crossref","unstructured":"Singh, Y., LAKHWINDER KAUR, A., & Neeru, N. (2020). CLOUD-BASED OPTIMIZED KEY FRAME EXTRACTION MODEL FOR VISUALLY IMPAIRED PERSONS. Advances in Mathematics: Scientific Journal.","DOI":"10.37418\/amsj.9.6.49"},{"issue":"4","key":"11379_CR32","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/S0262-8856(02)00021-5","volume":"20","author":"SH Ong","year":"2002","unstructured":"Ong, S. H., Yeo, N. C., Lee, K. H., Venkatesh, Y. V., & Cao, D. M. (2002). Segmentation of color images using a two-stage self-organizing network. Image and Vision Computing, 20(4), 279\u2013289. https:\/\/doi.org\/10.1016\/S0262-8856(02)00021-5","journal-title":"Image and Vision Computing"},{"issue":"1","key":"11379_CR33","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1177\/27.1.374628","volume":"27","author":"W Abmayr","year":"1979","unstructured":"Abmayr, W., Burger, G., & Soost, H. J. (1979). Progress report of TUDAB project for automated cancer cell detection. The Journal of Histochemistry and Cytochemistry, 27(1), 604\u2013612.","journal-title":"The Journal of Histochemistry and Cytochemistry"},{"issue":"7","key":"11379_CR34","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1177\/25.7.330719","volume":"25","author":"HM Aus","year":"1977","unstructured":"Aus, H. M., R\u00fcter, A., Meulen, V. T., Gunzer, U., & N\u00fcrnberger, R. (1977). Bone marrow cell scene segmentation by computer-aided color cytophotometry. The Journal of Histochemistry and Cytochemistry, 25(7), 662\u2013667.","journal-title":"The Journal of Histochemistry and Cytochemistry"},{"issue":"6","key":"11379_CR35","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1002\/cyto.990070605","volume":"7","author":"H Harms","year":"1986","unstructured":"Harms, H., Aus, H. M., Haucke, M., & Gunzer, U. (1986). Segmentation of stained blood cell images measured at high scanning density with high magnification and high numerical aperture optics. Cytometry, 7(6), 522\u2013531.","journal-title":"Cytometry"},{"issue":"9","key":"11379_CR36","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TBME.1985.325587","volume":"32","author":"L O\u2019Gorman","year":"1985","unstructured":"O\u2019Gorman, L., Sanderson, A. C., & Preston, K. J. (1985). A system for automated liver tissue imagery analysis: Methods and results. IEEE Transactions on Biomedical Engineering, 32(9), 696\u2013706.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"10","key":"11379_CR37","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1109\/10.536902","volume":"1996","author":"J-P Thiran","year":"1996","unstructured":"Thiran, J.-P., & Macq, B. (1996). Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Transactions on Biomedical Engineering, 1996(10), 1011\u20131020.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"11379_CR38","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1046\/j.1365-2818.1998.00397.x","volume":"192","author":"D Young","year":"1998","unstructured":"Young, D., Glasbey, C. A., Gray, A. J., & Martin, N. J. (1998). Towards automatic cell identification in DIC microscopy. Journal of Microscopy, 192(2), 186\u2013193.","journal-title":"Journal of Microscopy"},{"issue":"10","key":"11379_CR39","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1109\/TMI.2002.806292","volume":"21","author":"C Zimmer","year":"2002","unstructured":"Zimmer, C., Labruy\u00e8re, E., Meas-Yedid, V., Guill\u00e9n, N., & Olivo-Marin, J. C. (2002). Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: A tool for cell-based drug testing. IEEE Transactions on Medical Imaging, 21(10), 1212\u20131221.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"11379_CR40","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","volume":"16","author":"T Falk","year":"2019","unstructured":"Falk, T., et al. (2019). U-Net: Deep learning for cell counting, detection, and morphometry. Nature methods, 16(1), 67.","journal-title":"Nature methods"},{"issue":"10","key":"11379_CR41","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1002\/bit.26783","volume":"115","author":"Y Huang","year":"2018","unstructured":"Huang, Y., Bao, Y., Kwong, H. K., Chen, T. H., & Lam, M. L. (2018). Outline-etching image segmentation reveals enhanced cell chirality through intercellular alignment. Biotechnology and bioengineering, 115(10), 2595\u20132603.","journal-title":"Biotechnology and bioengineering"},{"issue":"1","key":"11379_CR42","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1109\/TMI.2018.2859478","volume":"38","author":"A Van Opbroek","year":"2019","unstructured":"Van Opbroek, A., Achterberg, H. C., Vernooij, M. W., & De Bruijne, M. (2019). Transfer learning for image segmentation by combining image weighting and kernel learning. IEEE transactions on medical imaging, 38(1), 213\u2013224.","journal-title":"IEEE transactions on medical imaging"},{"key":"11379_CR43","doi-asserted-by":"crossref","unstructured":"Lv, Shuxing, et al. \"Improved efficiency of urine cell image segmentation using droplet microfluidics technology.\" Cytometry Part A (2020).","DOI":"10.1002\/cyto.a.24296"},{"key":"11379_CR44","unstructured":"Jingwen, Z. H. U., and Yongmian Zhang. \"Method and system for multi-scale cell image segmentation using multipleparallel convolutional neural networks.\" U.S. Patent No. 10,846,566. 24 Nov. 2020."},{"key":"11379_CR45","doi-asserted-by":"publisher","DOI":"10.3791\/2204-v","author":"K Ongena","year":"2010","unstructured":"Ongena, K., Das, C., Smith, J. L., Gil, S., & Johnston, G. (2010). Determining cell number during cell culture using the sceptercell counter. Journal of visualized experiments: JoVE. https:\/\/doi.org\/10.3791\/2204-v","journal-title":"Journal of visualized experiments: JoVE"},{"issue":"3","key":"11379_CR46","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1046\/j.1365-2818.2001.00854.x","volume":"201","author":"CO De Solorzano","year":"2001","unstructured":"De Solorzano, C. O., Malladi, R., Leli\u00e8vre, S. A., & Lockett, S. J. (2001). Segmentation of nuclei and cells using membranerelated protein markers. Journal of Microscopy, 201(3), 404\u2013415.","journal-title":"Journal of Microscopy"},{"issue":"4","key":"11379_CR47","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1111\/j.1600-0854.2007.00538.x","volume":"8","author":"TJ Gniadek","year":"2007","unstructured":"Gniadek, T. J., & Warren, G. (2007). WatershedCounting3D: A new method for segmenting and counting punctatestructures from confocal image data. Traffic, 8(4), 339\u2013346.","journal-title":"Traffic"},{"issue":"1","key":"11379_CR48","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/S0925-2312(01)00642-7","volume":"48","author":"TW Nattkemper","year":"2002","unstructured":"Nattkemper, T. W., Wersing, H., Schubert, W., & Ritter, H. (2002). A neural network architecture for automatic segmentationof fluorescence micrographs. Neurocomputing, 48(1), 357\u2013367.","journal-title":"Neurocomputing"},{"key":"11379_CR49","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1049\/iet-ipr.2018.6361","volume":"14","author":"Y Singh","year":"2020","unstructured":"Singh, Y., & Kaur, L. (2020). Effective key-frame extraction approach using TSTBTC-BBA. IET Image Process., 14, 638\u2013647.","journal-title":"IET Image Process."},{"issue":"6","key":"11379_CR50","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TBME.2006.873538","volume":"53","author":"KZ Mao","year":"2006","unstructured":"Mao, K. Z., Zhao, P., & Pan, P. H. (2006). Supervised learning-based cell image segmentation for P53 immunohistochemistry. IEEE Transactions on Biomedical Engineering, 53(6), 1153\u20131163.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"3","key":"11379_CR51","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1109\/72.846739","volume":"14","author":"KM Lee","year":"2003","unstructured":"Lee, K. M., & Street, W. N. (2003). An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: Image processing and recognition. IEEE Transactions on Neural Networks, 14(3), 680\u2013687.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"1","key":"11379_CR52","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1002\/cyto.a.10079","volume":"56A","author":"G Lin","year":"2003","unstructured":"Lin, G., Adiga, U., Olson, K., Guzowski, J. F., Barnes, C. A., & Roysam, B. (2003). A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry Part A, 56A(1), 23\u201336.","journal-title":"Cytometry Part A"},{"key":"11379_CR53","doi-asserted-by":"crossref","unstructured":"C. Zheng, A. Long, Y. Volkov, A. Davies, D. Kelleher, and K. Ahmad, \"A cross-modal system for cell migration image annotation and retrieval,\" presented at the 2007 International Joint Conference on Neural Networks, Orlando, FL, 12\u201317, 2007","DOI":"10.1109\/IJCNN.2007.4371220"},{"key":"11379_CR54","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.compmedimag.2019.01.003","volume":"72","author":"FHD Ara\u00fajo","year":"2019","unstructured":"Ara\u00fajo, F. H. D., et al. (2019). Deep learning for cell image segmentation and ranking. Computerized Medical Imaging and Graphics, 72, 13\u201321.","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"4","key":"11379_CR55","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1109\/TMI.2018.2874104","volume":"38","author":"M Winter","year":"2018","unstructured":"Winter, M., et al. (2018). Separating touching cells using pixel replicated elliptical shape models. IEEE transactions on medical imaging, 38(4), 883\u2013893.","journal-title":"IEEE transactions on medical imaging"},{"issue":"7","key":"11379_CR56","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1016\/j.patcog.2007.11.006","volume":"41","author":"O Schmitt","year":"2008","unstructured":"Schmitt, O., & Hasse. (2008). Radial sysmetries based decomposition of cell clusters in binary and gray level images. Pattern Recognition, 41(7), 1905\u20131923.","journal-title":"Pattern Recognition"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11379-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-024-11379-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11379-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T08:08:39Z","timestamp":1721376519000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-024-11379-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":56,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["11379"],"URL":"https:\/\/doi.org\/10.1007\/s11277-024-11379-7","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"11 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}