{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:59:12Z","timestamp":1778255952652,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:00:00Z","timestamp":1672876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Croatian Science Foundation under the Project","award":["UIP-2017-05-4968"],"award-info":[{"award-number":["UIP-2017-05-4968"]}]},{"name":"Croatian Science Foundation under the Project","award":["IZIP 2022"],"award-info":[{"award-number":["IZIP 2022"]}]},{"name":"Croatian Science Foundation under the Project","award":["174B09119"],"award-info":[{"award-number":["174B09119"]}]},{"name":"Faculty of Electrical Engineering, Computer Science and Information Technology Osijek","award":["UIP-2017-05-4968"],"award-info":[{"award-number":["UIP-2017-05-4968"]}]},{"name":"Faculty of Electrical Engineering, Computer Science and Information Technology Osijek","award":["IZIP 2022"],"award-info":[{"award-number":["IZIP 2022"]}]},{"name":"Faculty of Electrical Engineering, Computer Science and Information Technology Osijek","award":["174B09119"],"award-info":[{"award-number":["174B09119"]}]},{"name":"Flanders AI Research Programme","award":["UIP-2017-05-4968"],"award-info":[{"award-number":["UIP-2017-05-4968"]}]},{"name":"Flanders AI Research Programme","award":["IZIP 2022"],"award-info":[{"award-number":["IZIP 2022"]}]},{"name":"Flanders AI Research Programme","award":["174B09119"],"award-info":[{"award-number":["174B09119"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Medical images are often of huge size, which presents a challenge in terms of memory requirements when training machine learning models. Commonly, the images are downsampled to overcome this challenge, but this leads to a loss of information. We present a general approach for training semantic segmentation neural networks on much smaller input sizes called Segment-then-Segment. To reduce the input size, we use image crops instead of downscaling. One neural network performs the initial segmentation on a downscaled image. This segmentation is then used to take the most salient crops of the full-resolution image with the surrounding context. Each crop is segmented using a second specially trained neural network. The segmentation masks of each crop are joined to form the final output image. We evaluate our approach on multiple medical image modalities (microscopy, colonoscopy, and CT) and show that this approach greatly improves segmentation performance with small network input sizes when compared to baseline models trained on downscaled images, especially in terms of pixel-wise recall.<\/jats:p>","DOI":"10.3390\/s23020633","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T03:19:54Z","timestamp":1672975194000},"page":"633","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Segment-then-Segment: Context-Preserving Crop-Based Segmentation for Large Biomedical Images"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-0781","authenticated-orcid":false,"given":"Marin","family":"Ben\u010devi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Computer Science and Information Technology, J. J. Strossmayer University, 31000 Osijek, Croatia"},{"name":"TELIN-GAIM, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-3792","authenticated-orcid":false,"given":"Yuming","family":"Qiu","sequence":"additional","affiliation":[{"name":"TELIN-GAIM, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium"},{"name":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0211-4568","authenticated-orcid":false,"given":"Irena","family":"Gali\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Computer Science and Information Technology, J. J. Strossmayer University, 31000 Osijek, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9322-4999","authenticated-orcid":false,"given":"Aleksandra","family":"Pi\u017eurica","sequence":"additional","affiliation":[{"name":"TELIN-GAIM, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, F., Hern\u00e1ndez-Cabronero, M., Sanchez, V., Marcellin, M., and Bilgin, A. (2017). The Current Role of Image Compression Standards in Medical Imaging. Information, 8.","DOI":"10.3390\/info8040131"},{"key":"ref_2","unstructured":"Tan, M., and Le, Q.V. (2020). 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