{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:57:47Z","timestamp":1781539067872,"version":"3.54.5"},"reference-count":23,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Ovarian cancer (OC) is typically treated with cytoreductive surgery (CRS). Hyperspectral imaging (HSI) is an emerging non-invasive, label-free technique that enables whole-area scanning, making it a promising tool for real-time tumour recognition. However, developing tumour recognition algorithms requires a foundational understanding of spectral variability in normal tissues. This study focusses on the in vivo spectral profiles of key abdominal and pelvic organs encountered during CRS, including the uterus, ovaries, intestines, mesentery, omentum, peritoneum, and fallopian tubes, and evaluates the potential for organ recognition using HSI data. Intraoperative HSI data were from healthy patients. Two machine learning models, a support vector machine (SVM) and a 3D convolutional neural network (3DCNN), were trained to classify the organs based on their spectral signatures. In total, 15 patients were included in the dataset. The 3DCNN slightly outperformed the SVM in terms of the average accuracy (0.889 vs. 0.878), sensitivity (0.648 vs. 0.604), specificity (0.936 vs. 0.930), and Dice Similarity Coefficient (0.595 vs. 0.569). This study demonstrates the feasibility of using HSI for organ differentiation in the clinical setting, although in some cases separability remains a challenge, especially when organs have similar spectra. This is a critical step towards a generalizable in vivo abdominal tumour recognition algorithm, by carefully investigating spectral fingerprints of abdominal tissues.<\/jats:p>","DOI":"10.3390\/jimaging12060262","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:00:48Z","timestamp":1781535648000},"page":"262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hyperspectral Fingerprints of Abdominal and Pelvic Organs"],"prefix":"10.3390","volume":"12","author":[{"given":"Laurie S.","family":"van de Weerd","sequence":"first","affiliation":[{"name":"Department of Gynaecological Oncology, Erasmus Medical Center Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands"},{"name":"Department of BioMechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nick J.","family":"van de Berg","sequence":"additional","affiliation":[{"name":"Department of Gynaecological Oncology, Erasmus Medical Center Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands"},{"name":"Department of BioMechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L. Lucia","family":"Rijstenberg","sequence":"additional","affiliation":[{"name":"Department of Pathology, Erasmus University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5665-5407","authenticated-orcid":false,"given":"Ralf L. O.","family":"van de Laar","sequence":"additional","affiliation":[{"name":"Department of Gynaecological Oncology, Erasmus Medical Center Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8899-7412","authenticated-orcid":false,"given":"Heleen J.","family":"van Beekhuizen","sequence":"additional","affiliation":[{"name":"Department of Gynaecological Oncology, Erasmus Medical Center Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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