{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T04:47:16Z","timestamp":1785646036521,"version":"3.56.0"},"reference-count":61,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001821","name":"Vienna Science and Technology Fund","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001821","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/01f9mc681","id-type":"ROR","asserted-by":"publisher"}]},{"name":"State of Lower Austria","award":["10.47379\/LS23006"],"award-info":[{"award-number":["10.47379\/LS23006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Nuclei instance segmentation in hematoxylin and eosin (H&amp;E)-stained images plays an important role in automated histological image analysis, with various applications in downstream tasks. While several machine learning and deep learning approaches have been proposed for nuclei instance segmentation, most research in this field focuses on developing new segmentation algorithms and benchmarking them on a limited number of arbitrarily selected public datasets. In this work, rather than focusing on model development, we focused on the datasets used for this task. Based on an extensive literature review, we identified manually annotated, publicly available datasets of H&amp;E-stained images for nuclei instance segmentation and standardized them into a unified input and annotation format. Using two state-of-the-art segmentation models, one based on convolutional neural networks (CNNs) and one based on a hybrid CNN and vision transformer architecture, we systematically evaluated and ranked these datasets based on their nuclei instance segmentation performance. Furthermore, we proposed a unified test set (NucFuse-test) for fair cross-dataset evaluation and a unified training set (NucFuse-train) for improved segmentation performance by merging images from multiple datasets. To the best of our knowledge, this is the first study to systematically benchmark and rank publicly available datasets for nuclei instance segmentation. By evaluating and ranking the datasets, performing comprehensive analyses, generating fused datasets, conducting external validation, and making our implementation publicly available, we provided a new protocol for training, testing, and evaluating nuclei instance segmentation models on H&amp;E-stained histological images.<\/jats:p>","DOI":"10.3390\/bioengineering13080886","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:20:18Z","timestamp":1785486018000},"page":"886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7468-1038","authenticated-orcid":false,"given":"Nima","family":"Torbati","sequence":"first","affiliation":[{"name":"Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems an der Donau, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4359-9546","authenticated-orcid":false,"given":"Anastasia","family":"Meshcheryakova","sequence":"additional","affiliation":[{"name":"Department of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria"},{"name":"Comprehensive Center for AI in Medicine (CAIM), Medical University of Vienna, 1090 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-9159","authenticated-orcid":false,"given":"Ramona","family":"Woitek","sequence":"additional","affiliation":[{"name":"Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems an der Donau, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0182-0954","authenticated-orcid":false,"given":"Sepideh","family":"Hatamikia","sequence":"additional","affiliation":[{"name":"Research Center for Medical Data Sciences and Artificial Intelligence (MeDSAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems an der Donau, Austria"},{"name":"Austrian Center for Medical Innovation and Technology, 2700 Wiener Neustadt, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8737-3592","authenticated-orcid":false,"given":"Diana","family":"Mechtcheriakova","sequence":"additional","affiliation":[{"name":"Department of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria"},{"name":"Comprehensive Center for AI in Medicine (CAIM), Medical University of Vienna, 1090 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5042-1442","authenticated-orcid":false,"given":"Amirreza","family":"Mahbod","sequence":"additional","affiliation":[{"name":"Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems an der Donau, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Omoush, S.A., Alzyoud, J.A.M., El-Omari, N.K.T., and Alzyoud, A.J.A. 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