{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:07:22Z","timestamp":1757617642755,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":14,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819638628"},{"type":"electronic","value":"9789819638635"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-3863-5_16","type":"book-chapter","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T03:40:58Z","timestamp":1743824458000},"page":"165-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dependency-Related Skin Lesion Bed and\u00a0Periwound Segmentation Trained on\u00a0Partially Annotated Clinical Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4191-2278","authenticated-orcid":false,"given":"Laura Valeria","family":"Perez-Herrera","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6692-9934","authenticated-orcid":false,"given":"Miriam","family":"Guti\u00e9rrez Fern\u00e1ndez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4394-7821","authenticated-orcid":false,"given":"Iker","family":"Perez de Albeniz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9819-841X","authenticated-orcid":false,"given":"Irene","family":"Joga","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5452-2141","authenticated-orcid":false,"given":"Andoni","family":"Beristain Iraola","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"issue":"3","key":"16_CR1","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1093\/ageing\/afh086","volume":"33","author":"G Bennett","year":"2004","unstructured":"Bennett, G., Dealey, C., Posnett, J.: The cost of pressure ulcers in the UK. Age Ageing 33(3), 230\u2013235 (2004)","journal-title":"Age Ageing"},{"issue":"6","key":"16_CR2","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1097\/WON.0000000000000481","volume":"45","author":"K LeBlanc","year":"2018","unstructured":"LeBlanc, K., Campbell, K.E., Wood, E., Beeckman, D.: Best practice recommendations for prevention and management of skin tears in aged skin: an overview. J. Wound Ostomy Continence Nurs.: Off. Publ. Wound Ostomy Continence Nurses Soc. 45(6), 540\u2013542 (2018)","journal-title":"J. Wound Ostomy Continence Nurs.: Off. Publ. Wound Ostomy Continence Nurses Soc."},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Leaper, D., Harding, K.: Wounds: Biology and management. Oxford University Press (1998)","DOI":"10.1093\/oso\/9780192623324.001.0001"},{"issue":"2","key":"16_CR4","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1097\/01.ASW.0000426714.57540.c4","volume":"26","author":"S Hunter","year":"2013","unstructured":"Hunter, S., Langemo, D., Thompson, P., et al.: Observations of periwound skin protection in venous ulcers: a comparison of treatments. Adv. Skin Wound Care 26(2), 61\u20136 (2013)","journal-title":"Adv. Skin Wound Care"},{"key":"16_CR5","first-page":"58","volume":"1","author":"J Bianchi","year":"2012","unstructured":"Bianchi, J.: Protecting the integrity of the periwound skin. Wound Essentials 1, 58\u201364 (2012)","journal-title":"Wound Essentials"},{"key":"16_CR6","unstructured":"Assessment, V.I.W.: Consensus document (2020)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Oota, S., et al.: WSNet: towards an effective method for wound image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3234\u20133243 (2023)","DOI":"10.1109\/WACV56688.2023.00325"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"706","DOI":"10.3390\/ijms24010706","volume":"24","author":"N Curti","year":"2023","unstructured":"Curti, N., et al.: Effectiveness of semi-supervised active learning in automated wound image segmentation. Int. J. Mol. Sci. 24, 706 (2023)","journal-title":"Int. J. Mol. Sci."},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: COSST: multi-organ segmentation with partially labeled datasets using comprehensive supervisions and self-training. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3354673"},{"key":"16_CR10","doi-asserted-by":"publisher","unstructured":"Abdi, Y., et al.: CPW-DICE: a novel center and pixel-based weighting for damage segmentation. Connect. Sci. 35(1) (2023). https:\/\/doi.org\/10.1080\/09540091.2023.2259115","DOI":"10.1080\/09540091.2023.2259115"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Sangalli, M., et al.: Spatial reasoning loss for weakly supervised segmentation of skin histological images. In: 21st IEEE International Symposium on Biomedical Imaging (2024)","DOI":"10.1109\/ISBI56570.2024.10635595"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"21897","DOI":"10.1038\/s41598-020-78799-w","volume":"10","author":"C Wang","year":"2020","unstructured":"Wang, C., et al.: Fully automatic wound segmentation with deep convolutional neural networks. Sci. Rep. 10, 21897 (2020). https:\/\/doi.org\/10.1038\/s41598-020-78799-w","journal-title":"Sci. Rep."},{"key":"16_CR13","unstructured":"MEDETEC. https:\/\/www.medetec.co.uk\/index.html. Accessed 21 June 2024"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., et al.: Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240\u2013248 (2017)","DOI":"10.1007\/978-3-319-67558-9_28"}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-3863-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T09:09:24Z","timestamp":1757149764000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-3863-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819638628","9789819638635"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-3863-5_16","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micad.org\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}