{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:36:06Z","timestamp":1740144966091,"version":"3.37.3"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1016\/j.bspc.2022.104491","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T17:16:52Z","timestamp":1671211012000},"page":"104491","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Convolutional neural networks-based method for skin hydration measurements in high resolution MRI"],"prefix":"10.1016","volume":"81","author":[{"given":"Rachida","family":"Zegour","sequence":"first","affiliation":[]},{"given":"Ahror","family":"Belaid","sequence":"additional","affiliation":[]},{"given":"Julien","family":"Ognard","sequence":"additional","affiliation":[]},{"given":"Douraied","family":"Ben Salem","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.bspc.2022.104491_b1","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1111\/srt.12654","article-title":"Edge detector-based automatic segmentation of the skin layers and application to moisturization in high-resolution 3 T magnetic resonance imaging","volume":"25","author":"Ognard","year":"2019","journal-title":"Skin Res. Technol."},{"issue":"5","key":"10.1016\/j.bspc.2022.104491_b2","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1097\/WON.0000000000000363","article-title":"Quantitation of 24-hour moisturization by electrical measurements of skin hydration","volume":"44","author":"Randall\u00a0Wickett","year":"2017","journal-title":"Wound Ostomy Cont. Nurs."},{"key":"10.1016\/j.bspc.2022.104491_b3","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1111\/srt.12257","article-title":"The investigation of the skin biophysical measurements focusing on daily activities, skin care habits, and gender differences","volume":"22","author":"Hari","year":"2016","journal-title":"Skin Res. Technol."},{"issue":"6","key":"10.1016\/j.bspc.2022.104491_b4","doi-asserted-by":"crossref","DOI":"10.1364\/BOE.7.002311","article-title":"Quantitative and simultaneous non-invasive measurement of skin hydration and sebum levels","volume":"7","author":"Ezerskaia","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.bspc.2022.104491_b5","first-page":"687","article-title":"Hydration of the skin surface","author":"Tagami","year":"2017","journal-title":"Textb. Aging Skin"},{"issue":"1","key":"10.1016\/j.bspc.2022.104491_b6","article-title":"The influences of skin visco-elasticity, hydration level and aging on the formation of wrinkles: a comprehensive and objective approach","volume":"19","author":"Woo\u00a0Choi","year":"2012","journal-title":"Skin Res. Technol."},{"issue":"3","key":"10.1016\/j.bspc.2022.104491_b7","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1111\/bjd.12134","article-title":"Assessment of skin barrier function in podoconiosis: measurement of stratum corneum hydration and transepidermal water loss","volume":"168","author":"Ferguson","year":"2013","journal-title":"Br. J. Dermatol."},{"issue":"2","key":"10.1016\/j.bspc.2022.104491_b8","first-page":"122","article-title":"In vivo and in vitro investigations of hydration effects of beauty care products by high-field MRI and NMR microscopy","volume":"11","author":"Szayna","year":"1998","journal-title":"Eur. Acad. Dermatol. Venereol."},{"issue":"2","key":"10.1016\/j.bspc.2022.104491_b9","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1111\/1523-1747.ep12462252","article-title":"Taylor. Electron probe analysis of human skin: determination of the water concentration profile","volume":"90","author":"Warner","year":"1988","journal-title":"J. Invest. Dermatol."},{"issue":"3","key":"10.1016\/j.bspc.2022.104491_b10","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1046\/j.1523-1747.2001.01258.x","article-title":"In vivo confocal Raman microspectroscopy of the skin: noninvasive determination of molecular concentration profiles","volume":"116","author":"Caspers","year":"2001","journal-title":"J. Invest. Dermatol."},{"issue":"2","key":"10.1016\/j.bspc.2022.104491_b11","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1111\/j.1473-2165.2007.00300.x","article-title":"Skin hydration: a review on its molecular mechanisms","volume":"6","author":"Verdier-S\u00e9vrain","year":"2007","journal-title":"J. Cosmetic Dermatol."},{"issue":"3","key":"10.1016\/j.bspc.2022.104491_b12","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.jdermsci.2006.06.003","article-title":"Skin barrier function, epidermal proliferation and differentiation in eczema","volume":"43","author":"Proksch","year":"2006","journal-title":"J. Dermatol. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2022.104491_b13","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1111\/j.1600-0846.2004.00047.x","article-title":"In vivo quantitative analysis of the effect of hydration (immersion and vaseline treatment) in skin layers using high-resolution MRI and magnetisation transfer contrast","volume":"10","author":"Mirrashed","year":"2005","journal-title":"Skin Res. Technol."},{"issue":"3","key":"10.1016\/j.bspc.2022.104491_b14","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1002\/mrm.22271","article-title":"In vivo high-resolution magnetic resonance skin imaging at 1.5 T and 3 T","volume":"63","author":"Barral","year":"2010","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"10.1016\/j.bspc.2022.104491_b15","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1111\/1523-1747.ep12478540","article-title":"In-vivo proton relaxation times analysis of the skin layers by magnetic resonance imaging","volume":"97","author":"Richard","year":"1991","journal-title":"J. Invest. Dermatol."},{"issue":"5","key":"10.1016\/j.bspc.2022.104491_b16","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1111\/j.1467-2494.1993.tb00076.x","article-title":"Moisturisation process in living human skin studied by magnetic resonance imaging microscopy","volume":"15","author":"Salter","year":"1993","journal-title":"Int. J. Cosmet. Sci."},{"issue":"8","key":"10.1016\/j.bspc.2022.104491_b17","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1111\/j.1468-3083.2010.03588.x","article-title":"Magnetic resonance imaging of the skin","volume":"24","author":"Stefanowska","year":"2010","journal-title":"J. Eur. Acad. Dermatol."},{"key":"10.1016\/j.bspc.2022.104491_b18","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/srt.12333","article-title":"In vivo skin moisturizing measurement by high resolution 3 t magnetic resonance imaging","volume":"23","author":"Mesrar","year":"2017","journal-title":"Skin Res. Technol."},{"issue":"5","key":"10.1016\/j.bspc.2022.104491_b19","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1111\/1523-1747.ep12472356","article-title":"Characterization of the skin in vivo by high resolution magnetic resonance imaging: Water behavior and age-related effects","volume":"100","author":"Richard","year":"1993","journal-title":"J. Invest. Dermatol."},{"issue":"4","key":"10.1016\/j.bspc.2022.104491_b20","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1034\/j.1600-0846.2000.006004205.x","article-title":"Study of three complementary techniques for measuring cutaneous hydration in vivo in human subjects: NMR spectroscopy, transient thermal transfer and corneometry - application to xerotic skin and cosmetics","volume":"6","author":"Girard","year":"2000","journal-title":"Skin Res. Technol."},{"key":"10.1016\/j.bspc.2022.104491_b21","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1111\/j.1600-0846.2007.00241.x","article-title":"In vivo visualization of hyaluronic acid injection by high spatial resolution T2 parametric magnetic resonance images","volume":"13","author":"Gensanne","year":"2007","journal-title":"Skin Res. Technol."},{"issue":"11","key":"10.1016\/j.bspc.2022.104491_b22","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1016\/j.drudis.2017.08.010","article-title":"From machine learning to deep learning: progress in machine intelligence for rational drug discovery","volume":"27","author":"Zhang","year":"2017","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.bspc.2022.104491_b23","series-title":"International MICCAI Brainlesion Workshop","first-page":"252","article-title":"Efficient embedding network for 3d brain tumor segmentation","author":"Messaoudi","year":"2020"},{"key":"10.1016\/j.bspc.2022.104491_b24","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11517-019-02050-6","article-title":"Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization","volume":"58","author":"Souadih","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.bspc.2022.104491_b25","unstructured":"Kh. Mukul, DenseNet -Densely Connected Convolutional Networks, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017."},{"key":"10.1016\/j.bspc.2022.104491_b26","doi-asserted-by":"crossref","DOI":"10.1186\/s13640-019-0467-y","article-title":"DermoNet: densely linked convolutional neural network for efficient skin lesion segmentation","author":"Baghersalimi","year":"2019","journal-title":"EURASIP J. Image Video Process."},{"key":"10.1016\/j.bspc.2022.104491_b27","unstructured":"M. Goyala, A. Oakley, B. Priyanka, et al. Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. 16 (2019) 206\u2013210. arXiv preprint arXiv."},{"issue":"2","key":"10.1016\/j.bspc.2022.104491_b28","first-page":"519","article-title":"Improving dermoscopic image segmentation with enhanced convolutional-deconvolutional networks","volume":"23","author":"Yading","year":"2017","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.bspc.2022.104491_b29","article-title":"Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks","author":"Bi","year":"2017","journal-title":"Med. J. Aust."},{"key":"10.1016\/j.bspc.2022.104491_b30","series-title":"Computer Science","article-title":"DenseNet star shape prior in fully convolutional networks for skin lesion segmentation","author":"Mirikharaji","year":"2018"},{"key":"10.1016\/j.bspc.2022.104491_b31","doi-asserted-by":"crossref","unstructured":"B. Bozorgtaba, et al., Investigating deep side layersfor skin lesion segmentation, in: IEEE International Symposium OnBiomedical Imaging, 2017.","DOI":"10.1109\/ISBI.2017.7950514"},{"issue":"39","key":"10.1016\/j.bspc.2022.104491_b32","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.bspc.2017.07.010","article-title":"Techniques and algorithms for computer aided diagnosis of pigmented skin lesions\u2014A review","author":"Pathana","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2022.104491_b33","article-title":"Dsnet: Automatic dermoscopic skin lesion segmentation","volume":"136","author":"Hasan","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2022.104491_b34","doi-asserted-by":"crossref","unstructured":"A. Lameski, J. Jovanov, et al., Skin lesion segmentation with deep learning, in: IEEE EUROCON 2019-18th International Conference on Smart Technologies, 2019.","DOI":"10.1109\/EUROCON.2019.8861636"},{"key":"10.1016\/j.bspc.2022.104491_b35","doi-asserted-by":"crossref","unstructured":"M.H. Jafari, M. Karimi, et al., Skin lesion segmentation in clinical images using deep learning, in: International Conference on Pattern Recognition, 2016.","DOI":"10.1109\/ICPR.2016.7899656"},{"key":"10.1016\/j.bspc.2022.104491_b36","doi-asserted-by":"crossref","unstructured":"R. Mishra, O. Daescui, Deep learning for skin lesion segmentation, in: IEEE International Conference on Bioinformatics and Biomedicine, BIBM, 2017.","DOI":"10.1109\/BIBM.2017.8217826"},{"key":"10.1016\/j.bspc.2022.104491_b37","doi-asserted-by":"crossref","unstructured":"S. Vesal, N. Ravikumar, et al., SkinNet: A Deep Learning Framework for Skin Lesion Segmentation, in: IEEE Symposium on Nuclear Science (NSS\/MIC), 2018.","DOI":"10.1109\/NSSMIC.2018.8824732"},{"key":"10.1016\/j.bspc.2022.104491_b38","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.cmpb.2019.07.005","article-title":"Efficient skin lesion segmentation using separable-unet with stochastic weight averaging","volume":"178","author":"Tang","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2022.104491_b39","doi-asserted-by":"crossref","unstructured":"Z. Al\u00a0Nazi, T. Azad, et al., Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning Approach with U-Net and DCNN-SVM, in: Proceedings of International Joint Conference on Computational Intelligence, 2019.","DOI":"10.1007\/978-981-13-7564-4_32"},{"issue":"4528","key":"10.1016\/j.bspc.2022.104491_b40","article-title":"Skin lesion segmentation by U-net with adaptive skip connection and structural awareness","volume":"11","author":"Phan","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2022.104491_b41","doi-asserted-by":"crossref","unstructured":"Zh Yue, et al., Feature Fusion for Segmentation and Classification of Skin Lesions, in: IEEE International Symposium on Biomedical Imaging, 2022, http:\/\/dx.doi.org\/10.1109\/ISBI52829.2022.9761474.","DOI":"10.1109\/ISBI52829.2022.9761474"},{"key":"10.1016\/j.bspc.2022.104491_b42","doi-asserted-by":"crossref","unstructured":"X. Zhang, W. Pan, et al., In-Vivo Skin Capacitive Image Classification Using AlexNet Convolutional Neural Network, in: IEEE 3rd International Conference on Image, Vision and Computing, ICIVC, 2018.","DOI":"10.1109\/ICIVC.2018.8492860"},{"key":"10.1016\/j.bspc.2022.104491_b43","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P.H. Fischer, T.H. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention. Medical Image Computing and Computer-Assisted Intervention, 2015, pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"04","key":"10.1016\/j.bspc.2022.104491_b44","first-page":"355","article-title":"Cartilage MRI T2 relaxation time mapping: Overview and applications","volume":"08","author":"Timothy","year":"2005","journal-title":"Semin. Musculoskeletal Radiol."},{"key":"10.1016\/j.bspc.2022.104491_b45","unstructured":"R. Weibin, Zhanjing Li, et al., An improved Canny edge detection algorithm, in: IEEE International Conference on Mechatronics and Automation, 2014."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809422009454?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809422009454?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T01:28:58Z","timestamp":1716859738000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809422009454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":45,"alternative-id":["S1746809422009454"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2022.104491","relation":{},"ISSN":["1746-8094"],"issn-type":[{"type":"print","value":"1746-8094"}],"subject":[],"published":{"date-parts":[[2023,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Convolutional neural networks-based method for skin hydration measurements in high resolution MRI","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2022.104491","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"104491"}}