{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:52:04Z","timestamp":1771703524417,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, Portugal","award":["UIDB\/04005\/2020"],"award-info":[{"award-number":["UIDB\/04005\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>Medical imaging is essential for pathology diagnosis and treatment, enhancing decision making and reducing costs, but despite various computational methodologies proposed to improve imaging modalities, further optimization is needed for broader acceptance. This study explores deep learning (DL) methodologies for classifying and segmenting pathological imaging data, optimizing models to accurately predict and generalize from training to new data. Different CNN and U-Net architectures are implemented for segmentation tasks, with their performance evaluated on histological image datasets using enhanced pre-processing techniques such as resizing, normalization, and data augmentation. These are trained, parameterized, and optimized using metrics such as accuracy, the DICE coefficient, and intersection over union (IoU). The experimental results show that the proposed method improves the efficiency of cell segmentation compared to networks, such as U-NET and W-UNET. The results show that the proposed pre-processing has improved the IoU from 0.9077 to 0.9675, about 7% better results; also, the values of the DICE coefficient obtained improved from 0.9215 to 0.9916, about 7% better results, surpassing the results reported in the literature.<\/jats:p>","DOI":"10.3390\/electronics13163335","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T06:28:51Z","timestamp":1724308131000},"page":"3335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improved Segmentation of Cellular Nuclei Using UNET Architectures for Enhanced Pathology Imaging"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3410-744X","authenticated-orcid":false,"given":"Sim\u00e3o","family":"Castro","sequence":"first","affiliation":[{"name":"The Center for Research in Organizations Markets and Industrial Management (COMEGI), Universidade Lus\u00edada, 1349-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8136-281X","authenticated-orcid":false,"given":"Vitor","family":"Pereira","sequence":"additional","affiliation":[{"name":"The Center for Research in Organizations Markets and Industrial Management (COMEGI), Universidade Lus\u00edada, 1349-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7929-0367","authenticated-orcid":false,"given":"Rui","family":"Silva","sequence":"additional","affiliation":[{"name":"The Center for Research in Organizations Markets and Industrial Management (COMEGI), Universidade Lus\u00edada, 1349-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","unstructured":"Metter, R.L.V., Beutel, J., and Kundel, H.L. (2000). Handbook of Medical Imaging, John Wiley & Sons."},{"key":"ref_2","first-page":"329","article-title":"The Importance of Perception Research in Medical Imaging","volume":"18","author":"Krupinski","year":"2000","journal-title":"Radiat. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.cmpb.2011.09.017","article-title":"Segmentation of Cervical Cell Nuclei in High-Resolution Microscopic Images: A New Algorithm and a Web-Based Software Framework","volume":"107","author":"Bergmeir","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rguibi, Z., Hajami, A., Zitouni, D., Elqaraoui, A., and Bedraoui, A. (2022). CXAI: Explaining Convolutional Neural Networks for Medical Imaging Diagnostic. Electronics, 11.","DOI":"10.3390\/electronics11111775"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gali\u0107, I., Habijan, M., Leventi\u0107, H., and Romi\u0107, K. (2023). Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods. Electronics, 12.","DOI":"10.3390\/electronics12214411"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.neucom.2019.08.103","article-title":"Robust Nuclei Segmentation in Histopathology Using ASPPU-Net and Boundary Refinement","volume":"408","author":"Wan","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jia, D., Zhang, C., Wu, N., Guo, Z., and Ge, H. (2021). Multi-Layer Segmentation Framework for Cell Nuclei Using Improved GVF Snake Model, Watershed, and Ellipse Fitting. Biomed. Signal Process. Control, 67.","DOI":"10.1016\/j.bspc.2021.102516"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102920","DOI":"10.1016\/j.media.2023.102920","article-title":"Segmentation in Large-Scale Cellular Electron Microscopy with Deep Learning: A Literature Survey","volume":"89","author":"Aswath","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, Z., Lim, S., Lu, Y., and Jung, S.-W. (2024). Reversed Domain Adaptation for Nuclei Segmentation-Based Pathological Image Classification. Comput. Biol. Med., 168.","DOI":"10.1016\/j.compbiomed.2023.107726"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A Survey on Deep Learning in Medical Image Analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yin, M., Li, Y., and Cai, Q. (2022). CSU-Net: A CNN-Transformer Parallel Network for Multimodal Brain Tumour Segmentation. Electronics, 11.","DOI":"10.3390\/electronics11142226"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Park, Y., Park, J., and Jang, G.-J. (2022). Efficient Perineural Invasion Detection of Histopathological Images Using U-Net. Electronics, 11.","DOI":"10.3390\/electronics11101649"},{"key":"ref_13","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","article-title":"State-of-the-Art in Artificial Neural Network Applications: A Survey","volume":"4","author":"Abiodun","year":"2018","journal-title":"Heliyon"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kiran, I., Raza, B., Ijaz, A., and Khan, M.A. (2022). DenseRes-Unet: Segmentation of Overlapped\/Clustered Nuclei from Multi Organ Histopathology Images. Comput. Biol. Med., 143.","DOI":"10.1016\/j.compbiomed.2022.105267"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, Z., Wu, M., Ye, Y., Wang, S., Cao, Y., and Yang, H. (2023). Nuclei Instance Segmentation Using a Transformer-Based Graph Convolutional Network and Contextual Information Augmentation. Comput. Biol. Med., 167.","DOI":"10.1016\/j.compbiomed.2023.107622"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101786","DOI":"10.1016\/j.media.2020.101786","article-title":"Triple U-Net: Hematoxylin-Aware Nuclei Segmentation with Progressive Dense Feature Aggregation","volume":"65","author":"Zhao","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","article-title":"Receptive Fields, Binocular Interaction and Functional Architecture in the Cat\u2019s Visual Cortex","volume":"160","author":"Hubel","year":"1962","journal-title":"J. Physiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.4249\/scholarpedia.3516","article-title":"Models of Visual Cortex","volume":"8","author":"Poggio","year":"2013","journal-title":"Scholarpedia"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kanavos, A., Papadimitriou, O., Al-Hussaeni, K., Maragoudakis, M., and Karamitsos, I. (2024). Advanced Convolutional Neural Networks for Precise White Blood Cell Subtype Classification in Medical Diagnostics. Electronics, 13.","DOI":"10.3390\/electronics13142818"},{"key":"ref_22","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":"2018","journal-title":"Nat. Methods"},{"key":"ref_23","unstructured":"Xia, X., and Kulis, B. (2017). W-Net: A Deep Model for Fully Unsupervised Image Segmentation. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Waqas, N., Safie, S.I., Kadir, K.A., and Khan, S. (2023). Knee Cartilage Segmentation Using Improved U-Net. Int. J. Adv. Comput. Sci. Appl., 14.","DOI":"10.14569\/IJACSA.2023.0140795"},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_26","unstructured":"(2023, January 10). Kaggle 2018 Data Science Bowl | Broad Bioimage Benchmark Collection. Available online: https:\/\/bbbc.broadinstitute.org\/bbbc\/BBBC038."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1038\/s41592-019-0612-7","article-title":"Nucleus Segmentation across Imaging Experiments: The 2018 Data Science Bowl","volume":"16","author":"Caicedo","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A Survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1016\/j.ajpath.2021.05.022","article-title":"Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images","volume":"191","author":"Durkee","year":"2021","journal-title":"Am. J. Pathol."},{"key":"ref_30","first-page":"3","article-title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","volume":"Volume 11045","author":"Zhou","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jadon, S. (2020, January 27\u201329). A Survey of Loss Functions for Semantic Segmentation. Proceedings of the 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Via del Mar, Chile.","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"ref_32","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the 33rd Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada. Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Yakopcic, C., Taha, T.M., and Asari, V.K. (2018, January 23\u201326). Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks Based U-Net (R2U-Net). 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