{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:29:23Z","timestamp":1772756963507,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation through the TALENT project","doi-asserted-by":"publisher","award":["PID2020-116417RB-C41"],"award-info":[{"award-number":["PID2020-116417RB-C41"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology.<\/jats:p>","DOI":"10.3390\/s24175654","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T10:54:11Z","timestamp":1725015251000},"page":"5654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3236-1236","authenticated-orcid":false,"given":"Gonzalo","family":"Rosa-Olmeda","sequence":"first","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7000-6289","authenticated-orcid":false,"given":"Manuel","family":"Villa","sequence":"additional","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5973-9991","authenticated-orcid":false,"given":"Sara","family":"Hiller-Vallina","sequence":"additional","affiliation":[{"name":"Pathology and Neurooncology Unit, Instituto de Investigaci\u00f3n Biom\u00e9dicas I+12, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain"},{"name":"Pathology and Neurooncology Unit, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-3440","authenticated-orcid":false,"given":"Miguel","family":"Chavarr\u00edas","sequence":"additional","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3610-4296","authenticated-orcid":false,"given":"Fernando","family":"Pescador","sequence":"additional","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems, Universidad Polit\u00e9cnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4032-0095","authenticated-orcid":false,"given":"Ricardo","family":"Gargini","sequence":"additional","affiliation":[{"name":"Pathology and Neurooncology Unit, Instituto de Investigaci\u00f3n Biom\u00e9dicas I+12, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain"},{"name":"Pathology and Neurooncology Unit, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bonert, M., Zafar, U., Maung, R., El-Shinnawy, I., Kak, I., Cutz, J., Naqvi, A., Juergens, R., Finley, C., and Salama, S. (2021). Evolution of anatomic pathology workload from 2011 to 2019 assessed in a regional hospital laboratory via 574,093 pathology reports. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0253876"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jahn, S.W., Plass, M., and Moinfar, F. (2020). Digital Pathology: Advantages, Limitations and Emerging Perspectives. J. Clin. Med., 9.","DOI":"10.3390\/jcm9113697"},{"key":"ref_3","first-page":"e44620","article-title":"Digital Pathology: Transforming Diagnosis in the Digital Age","volume":"15","author":"Kiran","year":"2023","journal-title":"Cureus"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/s41379-021-00919-2","article-title":"Digital pathology and artificial intelligence in translational medicine and clinical practice","volume":"35","author":"Baxi","year":"2022","journal-title":"Mod. Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1007\/s10278-020-00351-z","article-title":"Whole Slide Imaging (WSI) in Pathology: Current Perspectives and Future Directions","volume":"33","author":"Kumar","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_6","first-page":"23","article-title":"Whole slide imaging in pathology: Advantages, limitations, and emerging perspectives","volume":"7","author":"Farahani","year":"2015","journal-title":"Pathol. Lab. Med. Int."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.5858\/arpa.2019-0569-OA","article-title":"Digital whole slide imaging compared with light microscopy for primary diagnosis in surgical pathology: A multicenter, double-blinded, randomized study of 2045 cases","volume":"144","author":"Borowsky","year":"2020","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1016\/j.ajpath.2021.02.024","article-title":"Generative Deep Learning in Digital Pathology Workflows","volume":"191","author":"Morrison","year":"2021","journal-title":"Am. J. Pathol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rabilloud, N., Allaume, P., Acosta, O., De Crevoisier, R., Bourgade, R., Loussouarn, D., Rioux-Leclercq, N., Khene, Z.E., Mathieu, R., and Bensalah, K. (2023). Deep learning methodologies applied to digital pathology in prostate cancer: A systematic review. Diagnostics, 13.","DOI":"10.3390\/diagnostics13162676"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1002\/jemt.22391","article-title":"An automated system for whole microscopic image acquisition and analysis","volume":"77","author":"Bueno","year":"2014","journal-title":"Microsc. Res. Tech."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bian, Z., Guo, C., Jiang, S., Zhu, J., Wang, R., Song, P., Zhang, Z., Hoshino, K., and Zheng, G. (2020). Autofocusing technologies for whole slide imaging and automated microscopy. J. Biophotonics, 13.","DOI":"10.1002\/jbio.202000227"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13206-021-00041-0","article-title":"Hyperspectral imaging for clinical applications","volume":"16","author":"Yoon","year":"2022","journal-title":"BioChip J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e13311","DOI":"10.1111\/exsy.13311","article-title":"Hyperspectral imaging for early diagnosis of diseases: A review","volume":"40","author":"Mangotra","year":"2023","journal-title":"Expert Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Terentev, A., Dolzhenko, V., Fedotov, A., and Eremenko, D. (2022). Current state of hyperspectral remote sensing for early plant disease detection: A review. Sensors, 22.","DOI":"10.3390\/s22030757"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"031501","DOI":"10.1117\/1.JRS.15.031501","article-title":"Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review","volume":"15","author":"Peyghambari","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.crfs.2021.01.002","article-title":"Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review","volume":"4","author":"Saha","year":"2021","journal-title":"Curr. Res. Food Sci."},{"key":"ref_17","first-page":"100288","article-title":"Potential application of hyperspectral imaging in food grain quality inspection, evaluation and control during bulk storage","volume":"8","author":"Aviara","year":"2022","journal-title":"J. Agric. Food Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101678","DOI":"10.1016\/j.ecoinf.2022.101678","article-title":"A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications","volume":"69","author":"Khan","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3041","DOI":"10.1109\/JBHI.2021.3050483","article-title":"Spatial-Spectral Density Peaks-Based Discriminant Analysis for Membranous Nephropathy Classification Using Microscopic Hyperspectral Images","volume":"25","author":"Lv","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1364\/BOE.10.004568","article-title":"Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra","volume":"10","author":"Ishikawa","year":"2019","journal-title":"Biomed. Opt. Express"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Maktabi, M., Wichmann, Y., K\u00f6hler, H., Ahle, H., Lorenz, D., Bange, M., Braun, S., Gockel, I., Chalopin, C., and Thieme, R. (2022). Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-07524-6"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Halicek, M., Dormer, J.D., Little, J.V., Chen, A.Y., Myers, L., Sumer, B.D., and Fei, B. (2019). Hyperspectral imaging of head and neck squamous cell carcinoma for cancer margin detection in surgical specimens from 102 patients using deep learning. Cancers, 11.","DOI":"10.3390\/cancers11091367"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tran, M.H., Gomez, O., and Fei, B. (2023, January 28\u201331). An automatic whole-slide hyperspectral imaging microscope. Proceedings of the Label-Free Biomedical Imaging and Sensing (LBIS) 2023, SPIE, San Francisco, CA, USA.","DOI":"10.1117\/12.2650815"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109129","DOI":"10.1016\/j.microc.2023.109129","article-title":"An overview of pre-processing methods available for hyperspectral imaging applications","volume":"193","author":"Cozzolino","year":"2023","journal-title":"Microchem. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"10299","DOI":"10.1007\/s11042-022-12191-w","article-title":"Autofocus algorithm using optimized Laplace evaluation function and enhanced mountain climbing search algorithm","volume":"81","author":"Jia","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","unstructured":"OpenCV (2024, May 20). Laplace Operator. Available online: https:\/\/docs.opencv.org\/3.4\/d5\/db5\/tutorial_laplace_operator.html."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TCSVT.2012.2221191","article-title":"Overview of the High Efficiency Video Coding (HEVC) Standard","volume":"22","author":"Sullivan","year":"2012","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_28","unstructured":"OpenCV (2024, May 25). Affine Transformations. Available online: https:\/\/docs.opencv.org\/4.x\/d4\/d61\/tutorial_warp_affine.html."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_30","first-page":"323","article-title":"A comparison of SIFT and SURF","volume":"1","author":"Panchal","year":"2013","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"key":"ref_31","unstructured":"OpenCV (2024, May 25). Feature Matching. Available online: https:\/\/docs.opencv.org\/4.x\/dc\/dc3\/tutorial_py_matcher.html."},{"key":"ref_32","unstructured":"Pichette, J., Goossens, T., Vunckx, K., and Lambrechts, A. (February, January 31). Hyperspectral calibration method for CMOS-based hyperspectral sensors. Proceedings of the Photonic Instrumentation Engineering IV, SPIE, San Francisco, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Villa, M., Sancho, J., Villanueva, M., Urbanos, G., Sutradhar, P., Rosa, G., Vazquez, G., Martin, A., Chavarrias, M., and Perez, L. (2021, January 24\u201326). Stitching technique based on SURF for Hyperspectral Pushbroom Linescan Cameras. Proceedings of the 2021 XXXVI Conference on Design of Circuits and Integrated Systems (DCIS), Vila do Conde, Portugal.","DOI":"10.1109\/DCIS53048.2021.9666155"},{"key":"ref_34","unstructured":"Ximea (2024, June 01). xiSpec: Hyperspectral Imaging Camera Series. Am Mittelhafen 16, 48155 M\u00fcnster, Germany. Available online: https:\/\/www.ximea.com\/en\/products\/xilab-application-specific-oem-custom\/hyperspectral-cameras-based-on-usb3-xispec."},{"key":"ref_35","unstructured":"(2024, June 01). NV5 Geospatial Software. Available online: https:\/\/www.nv5geospatialsoftware.com\/docs\/ENVIHeaderFiles.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:45:58Z","timestamp":1760111158000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":35,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175654"],"URL":"https:\/\/doi.org\/10.3390\/s24175654","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]}}}