{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:10:12Z","timestamp":1760029812794,"version":"build-2065373602"},"reference-count":72,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA), and the National Center for Scientific and Technical Research (CNRST)","award":["ALKHAWARIZMI\/2020\/19"],"award-info":[{"award-number":["ALKHAWARIZMI\/2020\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The accurate segmentation of 3D spheroids is crucial in advancing biomedical research, particularly in understanding tumor development and testing therapeutic responses. As 3D spheroids emulate in vivo conditions more closely than traditional 2D cultures, efficient segmentation methods are essential for precise analysis. This study evaluates three prominent neural network architectures\u2014U-Net, HRNet, and DeepLabV3+\u2014for the segmentation of 3D spheroids, a critical challenge in biomedical image analysis. Through empirical analysis across a comprehensive Tumour Spheroid dataset, HRNet and DeepLabV3+ emerged as top performers, achieving high segmentation accuracy, with HRNet achieving 99.72% validation accuracy, a Dice coefficient of 96.70%, and a Jaccard coefficient of 93.62%. U-Net, although widely used in medical imaging, struggled to match the performance of the other models. The study also examines the impact of optimizers, with the Adam optimizer frequently causing overfitting, especially in U-Net models. Despite improvements with SGD and Adagrad, these optimizers did not surpass HRNet and DeepLabV3+. The study highlights the importance of selecting the right model\u2013optimizer combination for optimal segmentation.<\/jats:p>","DOI":"10.3390\/computers14030086","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T10:46:46Z","timestamp":1740739606000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Empirical Evaluation of Neural Network Architectures for 3D Spheroid Segmentation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0253-3006","authenticated-orcid":false,"given":"Fadoua","family":"Oudouar","sequence":"first","affiliation":[{"name":"Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat 10000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Bir-Jmel","sequence":"additional","affiliation":[{"name":"Research Center STIS, M2CS, Department of Applied Mathematics and Informatics, ENSAM, Mohammed V University in Rabat, Rabat 10000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanane","family":"Grissette","sequence":"additional","affiliation":[{"name":"MIMSC Laboratory, Higher School of Technology, Cadi Ayyad University, Marrakech 40001, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0893-820X","authenticated-orcid":false,"given":"Sidi Mohamed","family":"Douiri","sequence":"additional","affiliation":[{"name":"Laboratory of Mathematics, Computer Science & Applications-Security of Information, Department of Mathematics, Faculty of Sciences, Mohammed V University in Rabat, Rabat 10000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8904-5587","authenticated-orcid":false,"given":"Yassine","family":"Himeur","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8102-1556","authenticated-orcid":false,"given":"Sami","family":"Miniaoui","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-9243","authenticated-orcid":false,"given":"Shadi","family":"Atalla","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2784-5188","authenticated-orcid":false,"given":"Wathiq","family":"Mansoor","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.slasd.2023.03.006","article-title":"In Vitro three-dimensional (3D) cell culture tools for spheroid and organoid models","volume":"28","author":"Lee","year":"2023","journal-title":"SLAS Discov."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Ngo, T.K.N., Vu, M.A., and Tu, T.Y. (2024, January 16\u201322). Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPRW63382.2024.00531"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yun, C., Kim, S.H., Kim, K.M., Yang, M.H., Byun, M.R., Kim, J.H., Kwon, D., Pham, H.T., Kim, H.S., and Kim, J.H. (2024). Advantages of Using 3D Spheroid Culture Systems in Toxicological and Pharmacological Assessment for Osteogenesis Research. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25052512"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.jddst.2024.106033","article-title":"Spheroids in cancer research: Recent advances and opportunities","volume":"100","author":"Arora","year":"2024","journal-title":"J. Drug Deliv. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jadhav, S., Rath, S.N., and Roopavath, U.K. (2024). A Review on Multicellular Spheroids and Organoids for Breast Cancer Diagnosis and Therapy. Biomed. Mater. Devices, 1\u201323.","DOI":"10.1007\/s44174-024-00225-w"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Feng, L., Pan, R., Ning, K., Sun, W., Chen, Y., Xie, Y., Wang, M., Li, Y., and Yu, L. (2025). The impact of 3D tumor spheroid maturity on cell migration and invasion dynamics. Biochem. Eng. J., 213.","DOI":"10.1016\/j.bej.2024.109567"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ascione, F., Ferraro, R., Dogra, P., Cristini, V., Guido, S., and Caserta, S. (2024). Gradient-induced instability in tumour spheroids unveils the impact of microenvironmental nutrient changes. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-69570-6"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"149","DOI":"10.3390\/organoids1020012","article-title":"3D tumor spheroid and organoid to model tumor microenvironment for cancer immunotherapy","volume":"1","author":"Zhu","year":"2022","journal-title":"Organoids"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Piccinini, F., Peirsman, A., Stellato, M., Pyun, J.c., Tumedei, M.M., Tazzari, M., De Wever, O., Tesei, A., Martinelli, G., and Castellani, G. (2023). Deep learning-based tool for morphotypic analysis of 3D multicellular spheroids. J. Mech. Med. Biol., 23.","DOI":"10.1142\/S0219519423400341"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2202609","DOI":"10.1002\/adhm.202202609","article-title":"3D Biomimetic Models to Reconstitute Tumor Microenvironment In Vitro: Spheroids, Organoids, and Tumor-on-a-Chip","volume":"12","author":"Li","year":"2023","journal-title":"Adv. Healthc. Mater."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104014","DOI":"10.1016\/j.cag.2024.104014","article-title":"From superpixels to foundational models: An overview of unsupervised and generalizable image segmentation","volume":"123","author":"Rodrigues","year":"2024","journal-title":"Comput. Graph."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Koval, V., Zahorodnia, D., and Adamiv, O. (2019, January 4\u20137). An Image Segmentation Method for Obstacle Detection in a Mobile Robot Environment. Proceedings of the 2019 9th International Conference on Advanced Computer Information Technologies (ACIT), Ceske Budejovice, Czech Republic.","DOI":"10.1109\/ACITT.2019.8779903"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cageo.2016.12.001","article-title":"An interactive image segmentation method for lithological boundary detection: A rapid mapping tool for geologists","volume":"100","author":"Vasuki","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"200483","DOI":"10.1016\/j.fri.2021.200483","article-title":"Image segmentation of post-mortem computed tomography data in forensic imaging: Methods and applications","volume":"28","author":"Ebert","year":"2022","journal-title":"Forensic Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116399","DOI":"10.1016\/j.eswa.2021.116399","article-title":"IRUNet for medical image segmentation","volume":"191","author":"Hoorali","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, M. (2022, January 28\u201330). Momentum Contrast Learning for Aerial Image Segmentation and Precision Agriculture Analysis. Proceedings of the 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), Xi\u2019an, China.","DOI":"10.1109\/ICICML57342.2022.10009891"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kaymak, \u00c7., and U\u00e7ar, A. (2019, January 21\u201322). Semantic Image Segmentation for Autonomous Driving Using Fully Convolutional Networks. Proceedings of the 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkey.","DOI":"10.1109\/IDAP.2019.8875923"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Grexa, I., Diosdi, A., Harmati, M., Kriston, A., Moshkov, N., Buzas, K., Pieti\u00e4inen, V., Koos, K., and Horvath, P. (2021). SpheroidPicker for automated 3D cell culture manipulation using deep learning. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-94217-1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Habchi, Y., Himeur, Y., Kheddar, H., Boukabou, A., Atalla, S., Chouchane, A., Ouamane, A., and Mansoor, W. (2023). Ai in thyroid cancer diagnosis: Techniques, trends, and future directions. Systems, 11.","DOI":"10.3390\/systems11100519"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101945","DOI":"10.1016\/j.inffus.2023.101945","article-title":"Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends","volume":"100","author":"Arco","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s10462-023-10621-1","article-title":"A systematic review of deep learning based image segmentation to detect polyp","volume":"57","author":"Gupta","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Himeur, Y., Aburaed, N., Elharrouss, O., Varlamis, I., Atalla, S., Mansoor, W., and Ahmad, H.A. (2024). Applications of Knowledge Distillation in Remote Sensing: A Survey. arXiv.","DOI":"10.1016\/j.inffus.2024.102742"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1109\/TMI.2024.3363190","article-title":"Scribformer: Transformer makes cnn work better for scribble-based medical image segmentation","volume":"43","author":"Li","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"127105","DOI":"10.1016\/j.neucom.2023.127105","article-title":"Deep style transfer to deal with the domain shift problem on spheroid segmentation","volume":"569","author":"Heras","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shahbazi, A.S., Irandoost, F., Mahdavian, R., Shojaeilangari, S., Allahvardi, A., and Naderi-Manesh, H. (2025). A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics. BMC Bioinform., 26.","DOI":"10.1186\/s12859-024-06031-x"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sampaio Da Silva, C., Boos, J.A., Goldowsky, J., Blache, M., Schmid, N., Heinemann, T., Netsch, C., Luongo, F., Boder-Pasche, S., and Weder, G. (2024). High-throughput platform for label-free sorting of 3D spheroids using deep learning. Front. Bioeng. Biotechnol., 12.","DOI":"10.3389\/fbioe.2024.1432737"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lacalle, D., Castro-Abril, H.A., Randelovic, T., Dom\u00ednguez, C., Heras, J., Mata, E., Mata, G., M\u00e9ndez, Y., Pascual, V., and Ochoa, I. (2021). SpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation. Comput. Methods Programs Biomed., 200.","DOI":"10.1016\/j.cmpb.2020.105837"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Olofsson, K., Carannante, V., Takai, M., \u00d6nfelt, B., and Wiklund, M. (2021). Single cell organization and cell cycle characterization of DNA stained multicellular tumor spheroids. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-96288-6"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cigla, C., and Alatan, A.A. (2008, January 12\u201315). Region-based image segmentation via graph cuts. Proceedings of the 2008 15th IEEE International Conference on Image Processing, San Diego, CA, USA.","DOI":"10.1109\/ICIP.2008.4712244"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sharma, M., and Bhattacharya, M. (2019, January 18\u201321). Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bio-inspired Clustering Technique. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.","DOI":"10.1109\/BIBM47256.2019.8982974"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","article-title":"Teaching\u2013learning-based optimization: A novel method for constrained mechanical design optimization problems","volume":"43","author":"Rao","year":"2011","journal-title":"Comput.-Aided Des."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.isprsjprs.2022.08.024","article-title":"A hybrid image segmentation method for building extraction from high-resolution RGB images","volume":"192","author":"Hossain","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fallah, F., Yang, B., Walter, S.S., and Bamberg, F. (2018, January 19\u201321). Hierarchical Feature-learning Graph-based Segmentation of Fat-Water MR Images. Proceedings of the 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, Poland.","DOI":"10.23919\/SPA.2018.8563415"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Luo, D., Zeng, W., Chen, J., and Tang, W. (2021). Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application. Front. Med. Technol., 3.","DOI":"10.3389\/fmedt.2021.767836"},{"key":"ref_35","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":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1016\/j.eswa.2013.10.059","article-title":"Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur\u2019s entropy","volume":"41","author":"Bhandari","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Baydoun, M., and Al-Alaoui, M.A. (2015, January 9\u201310). Modified edge detection for segmentation. Proceedings of the 2015 International Symposium on Signals, Circuits and Systems (ISSCS), Iasi, Romania.","DOI":"10.1109\/ISSCS.2015.7204001"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bechar, A., Elmir, Y., Himeur, Y., Medjoudj, R., and Amira, A. (2024). Federated and Transfer Learning for Cancer Detection Based on Image Analysis. arXiv.","DOI":"10.1016\/j.procs.2024.06.373"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1016\/j.procs.2024.06.373","article-title":"Transfer Learning for Cancer Detection based on Images Analysis","volume":"239","author":"Bechar","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hamza, A., Lekouaghet, B., and Himeur, Y. (2023, January 8\u20139). Hybrid whale-mud-ring optimization for precise color skin cancer image segmentation. Proceedings of the 2023 6th International Conference on Signal Processing and Information Security (ICSPIS), Dubai, United Arab Emirates.","DOI":"10.1109\/ICSPIS60075.2023.10343708"},{"key":"ref_41","unstructured":"Habchi, Y., Kheddar, H., Himeur, Y., Boukabou, A., Atalla, S., Mansoor, W., and Al-Ahmad, H. (2024). Deep Transfer Learning for Kidney Cancer Diagnosis. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Khalifa, M., and Albadawy, M. (2024). Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions. Comput. Methods Programs Biomed. Update, 5.","DOI":"10.1016\/j.cmpbup.2024.100148"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104241","DOI":"10.1016\/j.ajp.2024.104241","article-title":"Leveraging AI for the diagnosis and treatment of autism spectrum disorder: Current trends and future prospects","volume":"101","author":"Wankhede","year":"2024","journal-title":"Asian J. Psychiatry"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s10916-023-02031-1","article-title":"Optimizing gene selection and cancer classification with hybrid sine cosine and cuckoo search algorithm","volume":"48","author":"Yaqoob","year":"2024","journal-title":"J. Med. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Alharbi, F., and Vakanski, A. (2023). Machine learning methods for cancer classification using gene expression data: A review. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020173"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13667","DOI":"10.1007\/s00521-021-05997-6","article-title":"A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis","volume":"33","author":"Stephan","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Thawkar, S., Sharma, S., Khanna, M., and kumar Singh, L. (2021). Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and ant lion optimizer. Comput. Biol. Med., 139.","DOI":"10.1016\/j.compbiomed.2021.104968"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bir-Jmel, A., Douiri, S.M., Bernoussi, S.E., Maafiri, A., Himeur, Y., Atalla, S., Mansoor, W., and Al-Ahmad, H. (2024). GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification. Computers, 13.","DOI":"10.3390\/computers13040093"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, J., Liang, K., and Song, X. (2022). Logistic regression with adaptive sparse group lasso penalty and its application in acute leukemia diagnosis. Comput. Biol. Med., 141.","DOI":"10.1016\/j.compbiomed.2021.105154"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tian, L., Li, X., Zheng, H., Wang, L., Qin, Y., and Cai, J. (2022). Fisher discriminant model based on LASSO logistic regression for computed tomography imaging diagnosis of pelvic rhabdomyosarcoma in children. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-20051-8"},{"key":"ref_51","unstructured":"Castillo, D.L. (2024, October 21). Deep-Tumour-Spheroid. GitHub. 2023. Available online: https:\/\/github.com\/WaterKnight1998\/Deep-Tumour-Spheroid."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1017\/S1431927619014983","article-title":"Quantitative Assessment of Anti-Cancer Drug Efficacy From Coregistered Mass Spectrometry and Fluorescence Microscopy Images of Multicellular Tumor Spheroids","volume":"25","author":"Kozubek","year":"2019","journal-title":"Microsc. Microanal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2948","DOI":"10.1093\/bioinformatics\/btaa029","article-title":"3D-Cell-Annotator: An open-source active surface tool for single-cell segmentation in 3D microscopy images","volume":"36","author":"Tasnadi","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Schmitz, A., Fischer, S.C., Mattheyer, C., Pampaloni, F., and Stelzer, E.H.K. (2017). Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids. Sci. Rep., 7.","DOI":"10.1038\/srep43693"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5517","DOI":"10.3390\/ijms16035517","article-title":"Three-Dimensional Cell Culture: A Breakthrough in Vivo","volume":"16","author":"Antoni","year":"2015","journal-title":"Int. J. Mol. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Barbosa, M.A.G., Xavier, C.P.R., Pereira, R.F., Petrikait\u0117, V., and Vasconcelos, M.H. (2021). 3D Cell Culture Models as Recapitulators of the Tumor Microenvironment for the Screening of Anti-Cancer Drugs. Cancers, 14.","DOI":"10.3390\/cancers14010190"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1039\/c3tx20086h","article-title":"HepG2\/C3A 3D spheroids exhibit stable physiological functionality for at least 24 days after recovering from trypsinisation","volume":"2","author":"Wrzesinski","year":"2013","journal-title":"Toxicol. Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Rousseau, D., Huaman, R., Rasti, P., and Riviere, C. (2018, January 22\u201326). Supervised machine learning for 3D light microscopy without manual annotation: Application to spheroids. Proceedings of the Unconventional Optical Imaging, Strasbourg, France.","DOI":"10.1117\/12.2303706"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Vaidyanathan, K., Wang, C., Krajnik, A., Yu, Y., Choi, M., Lin, B., and Bae, Y. (2021). A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-02683-4"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kecheril Sadanandan, S., Karlsson, J., and Wahlby, C. (2017, January 11\u201317). Spheroid segmentation using multiscale deep adversarial networks. Proceedings of the IEEE International Conference on Computer Vision Workshops, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW.2017.11"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Khoshdeli, M., Winkelmaier, G., and Parvin, B. (2018, January 18\u201323). Multilayer encoder-decoder network for 3D nuclear segmentation in spheroid models of human mammary epithelial cell lines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00300"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"334","DOI":"10.26415\/2572-004X-vol3iss1p334-346","article-title":"Automatic segmentation of the sphenoid sinus in CT-scans volume with deepmedics 3D CNN architecture: Array","volume":"3","author":"Souadih","year":"2019","journal-title":"Med. Technol. J."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1002\/cyto.a.23701","article-title":"Deep Learning in Image Cytometry: A Review","volume":"95","author":"Gupta","year":"2018","journal-title":"Cytometry Part A"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wang, L. (2022). Deep Learning Techniques to Diagnose Lung Cancer. Cancers, 14.","DOI":"10.3390\/cancers14225569"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ahmad, A., Goodarzi, S., Frindel, C., Recher, G., Riviere, C., and Rousseau, D. (2021). Clearing spheroids for 3D fluorescent microscopy: Combining safe and soft chemicals with deep convolutional neural network. Cold Spring Harb. Lab.","DOI":"10.1101\/2021.01.31.428996"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ma, N., Sun, X., Li, Q., Zeng, Y., Chen, F., and Gu, Z. (2021). Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition. Biomaterials, 272.","DOI":"10.1016\/j.biomaterials.2021.120770"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1515\/cdbme-2022-1084","article-title":"Annotation Efforts in Image Segmentation can be Reduced by Neural Network Bootstrapping","volume":"8","author":"Rettenberger","year":"2022","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"59187","DOI":"10.7554\/eLife.59187","article-title":"3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images","volume":"10","author":"Wen","year":"2021","journal-title":"eLife"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 13\u201317). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Lecture Notes in Computer Science, Washington, DC, USA.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201323). DenseASPP for Semantic Segmentation in Street Scenes. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_71","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., and Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Howard, J., and Gugger, S. (2020). Fastai: A Layered API for Deep Learning. Information, 11.","DOI":"10.3390\/info11020108"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/3\/86\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:44:37Z","timestamp":1760028277000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/3\/86"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,28]]},"references-count":72,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["computers14030086"],"URL":"https:\/\/doi.org\/10.3390\/computers14030086","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2025,2,28]]}}}