{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:27Z","timestamp":1740108087899,"version":"3.37.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62003224"],"award-info":[{"award-number":["62003224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00521-024-09544-x","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T14:02:01Z","timestamp":1709301721000},"page":"8711-8725","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Active phase recognition method of hydrogenation catalyst based on multi-feature fusion Mask CenterNet"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4344-446X","authenticated-orcid":false,"given":"Zhujun","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianhe","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haobin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ailin","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"issue":"9","key":"9544_CR1","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1002\/sia.6478","volume":"50","author":"S Wang","year":"2018","unstructured":"Wang S et al (2018) Structure-activity relationship of supported au catalysts with high catalytic activity by modifying the inactive supports. Surf Interface Anal 50(9):843\u2013850. https:\/\/doi.org\/10.1002\/sia.6478","journal-title":"Surf Interface Anal"},{"key":"9544_CR2","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.fuproc.2017.02.018","volume":"160","author":"H Liu","year":"2017","unstructured":"Liu H et al (2017) Pvp-assisted synthesis of unsupported nimo catalysts with enhanced hydrodesulfurization activity. Fuel Process Technol 160:93\u2013101. https:\/\/doi.org\/10.1016\/j.fuproc.2017.02.018","journal-title":"Fuel Process Technol"},{"issue":"13\u201314","key":"9544_CR3","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1007\/s11244-016-0628-5","volume":"59","author":"A Shipitcyna","year":"2016","unstructured":"Shipitcyna A et al (2016) Characterization and activity of Pd\u2013Ir catalysts in Co and C3h6 oxidation under stoichiometric conditions. Top Catal 59(13\u201314):1097\u20131103. https:\/\/doi.org\/10.1007\/s11244-016-0628-5","journal-title":"Top Catal"},{"issue":"4","key":"9544_CR4","doi-asserted-by":"publisher","first-page":"589","DOI":"10.5094\/apr.2015.066","volume":"6","author":"S Lokhande","year":"2015","unstructured":"Lokhande S et al (2015) High catalytic activity of Pt-Pd containing Usy Zeolite catalyst for low temperature co oxidation from industrial off gases. Atmos Pollut Res 6(4):589\u2013595. https:\/\/doi.org\/10.5094\/apr.2015.066","journal-title":"Atmos Pollut Res"},{"key":"9544_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.synthmet.2019.116260","author":"NM Dat","year":"2020","unstructured":"Dat NM et al (2020) Synthesis of silver\/reduced graphene oxide for antibacterial activity and catalytic reduction of organic dyes. Synth Metals. https:\/\/doi.org\/10.1016\/j.synthmet.2019.116260","journal-title":"Synth Metals"},{"issue":"1","key":"9544_CR6","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TNANO.2011.2162249","volume":"11","author":"Y Xu","year":"2012","unstructured":"Xu Y et al (2012) Catalyst activity enhancement of PtRu\/CB for methanol oxidation by carbon nanotube doping. IEEE Trans Nanotechnol 11(1):148\u2013151. https:\/\/doi.org\/10.1109\/TNANO.2011.2162249","journal-title":"IEEE Trans Nanotechnol"},{"issue":"14","key":"9544_CR7","doi-asserted-by":"publisher","first-page":"9915","DOI":"10.1007\/s00521-019-04516-y","volume":"32","author":"MM Fraz","year":"2020","unstructured":"Fraz MM et al (2020) Fabnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer. Neural Comput Appl 32(14):9915\u20139928. https:\/\/doi.org\/10.1007\/s00521-019-04516-y","journal-title":"Neural Comput Appl"},{"key":"9544_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06719-8","author":"S Gite","year":"2022","unstructured":"Gite S et al (2022) Enhanced lung image segmentation using deep learning. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06719-8","journal-title":"Neural Comput Appl"},{"issue":"13","key":"9544_CR9","doi-asserted-by":"publisher","first-page":"9627","DOI":"10.1007\/s00521-023-08199-4","volume":"35","author":"Li-Ying Hao","year":"2023","unstructured":"Hao Li-Ying et al (2023) Trca-Net: stronger U structured network for human image segmentation. Neural Comput Appl 35(13):9627\u20139635. https:\/\/doi.org\/10.1007\/s00521-023-08199-4","journal-title":"Neural Comput Appl"},{"issue":"21","key":"9544_CR10","doi-asserted-by":"publisher","first-page":"14991","DOI":"10.1007\/s00521-021-06134-z","volume":"33","author":"Amrita Kaur","year":"2021","unstructured":"Kaur Amrita et al (2021) Ga-Unet: Unet-based framework for segmentation of 2d and 3d medical images applicable on heterogeneous datasets. Neural Comput Appl 33(21):14991\u201315025. https:\/\/doi.org\/10.1007\/s00521-021-06134-z","journal-title":"Neural Comput Appl"},{"key":"9544_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06554-x","author":"G Sun","year":"2021","unstructured":"Sun G et al (2021) Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06554-x","journal-title":"Neural Comput Appl"},{"issue":"14","key":"9544_CR12","doi-asserted-by":"publisher","first-page":"8261","DOI":"10.1007\/s00521-020-04961-0","volume":"33","author":"Zijiang Zhu","year":"2021","unstructured":"Zhu Zijiang et al (2021) Indoor scene segmentation algorithm based on full convolutional neural network. Neural Comput Appl 33(14):8261\u20138273. https:\/\/doi.org\/10.1007\/s00521-020-04961-0","journal-title":"Neural Comput Appl"},{"issue":"12","key":"9544_CR13","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1109\/42.897818","volume":"19","author":"MS Pattichis","year":"2000","unstructured":"Pattichis MS et al (2000) AM\u2013FM texture segmentation in electron microscopic muscle imaging. IEEE Trans Med Imaging 19(12):1253\u20131257. https:\/\/doi.org\/10.1109\/42.897818","journal-title":"IEEE Trans Med Imaging"},{"key":"9544_CR14","doi-asserted-by":"publisher","unstructured":"Agarwal S, et al. (2023) Comparing U-Net and mask R-CNN algorithms for deep learning-based segmentation of electron microscopy images containing cavities for nuclear reactor applications. In: 2023 3rd International conference on electrical, computer, communications and mechatronics engineering (ICECCME), pp.148-151, https:\/\/doi.org\/10.1109\/ICECCME57830.2023.10252280","DOI":"10.1109\/ICECCME57830.2023.10252280"},{"key":"9544_CR15","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1587\/transfun.E98.A.1114","volume":"E98.A","author":"J Zhang","year":"2015","unstructured":"Zhang J et al (2015) Context-based segmentation of renal corpuscle from microscope renal biopsy image sequence. IEICE Trans Fundament Electron Commun Comput Sci E98.A:1114\u20131121. https:\/\/doi.org\/10.1587\/transfun.E98.A.1114","journal-title":"IEICE Trans Fundament Electron Commun Comput Sci"},{"key":"9544_CR16","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1111\/jmi.13110","volume":"288","author":"CG Bell","year":"2022","unstructured":"Bell CG et al (2022) Trainable segmentation for transmission electron microscope images of inorganic nanoparticles. J Microsc 288:169\u2013184. https:\/\/doi.org\/10.1111\/jmi.13110","journal-title":"J Microsc"},{"key":"9544_CR17","doi-asserted-by":"publisher","first-page":"3541","DOI":"10.1002\/jemt.24206","volume":"85","author":"ZZ You","year":"2022","unstructured":"You ZZ et al (2022) Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images. Microsc Res Tech 85:3541\u20133552. https:\/\/doi.org\/10.1002\/jemt.24206","journal-title":"Microsc Res Tech"},{"key":"9544_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2020.00025","author":"XB Liu","year":"2020","unstructured":"Liu XB et al (2020) Deep learning for cardiac image segmentation: a review. Front Cardiovasc Med. https:\/\/doi.org\/10.3389\/fcvm.2020.00025","journal-title":"Front Cardiovasc Med"},{"key":"9544_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-018-2375-z","author":"Y Al-Kofahi","year":"2018","unstructured":"Al-Kofahi Y et al (2018) A deep learning-based algorithm for 2-D cell segmentation in microscopy images. BMC Bioinformatics. https:\/\/doi.org\/10.1186\/s12859-018-2375-z","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"9544_CR20","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/TNANO.2011.2162249","volume":"21","author":"Y Xu","year":"2018","unstructured":"Xu Y et al (2018) Deep learning approaches in electron microscopy imaging for mitochondria segmentation. Int J Data Min Bioinform 21(2):91\u2013106. https:\/\/doi.org\/10.1109\/TNANO.2011.2162249","journal-title":"Int J Data Min Bioinform"},{"key":"9544_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.micron.2021.103044","author":"I Nikishin","year":"2021","unstructured":"Nikishin I et al (2021) Scanev-a neural network-based tool for the automated detection of extracellular vesicles in TEM images. Micron. https:\/\/doi.org\/10.1016\/j.micron.2021.103044","journal-title":"Micron"},{"key":"9544_CR22","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.micron.2019.02.009","volume":"120","author":"AB Oktay","year":"2019","unstructured":"Oktay AB, Gurses A (2019) Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images. Micron 120:113\u2013119. https:\/\/doi.org\/10.1016\/j.micron.2019.02.009","journal-title":"Micron"},{"key":"9544_CR23","doi-asserted-by":"publisher","first-page":"5688","DOI":"10.1016\/j.csbj.2021.10.001","volume":"19","author":"JS Rey","year":"2021","unstructured":"Rey JS et al (2021) Deep-learning in situ classification of Hiv-1 virion morphology. Comput Struct Biotechnol J 19:5688\u20135700. https:\/\/doi.org\/10.1016\/j.csbj.2021.10.001","journal-title":"Comput Struct Biotechnol J"},{"key":"9544_CR24","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.csbj.2022.11.0622001-0370","volume":"21","author":"MCM Aboy-Pardal","year":"2023","unstructured":"Aboy-Pardal MCM et al (2023) A deep learning-based tool for the automated detection and analysis of caveolae in transmission electron microscopy images. Comput Struct Biotechnol J 21:224\u2013237. https:\/\/doi.org\/10.1016\/j.csbj.2022.11.0622001-0370","journal-title":"Comput Struct Biotechnol J"},{"issue":"30","key":"9544_CR25","doi-asserted-by":"publisher","first-page":"10761","DOI":"10.1039\/d2nr01029a","volume":"14","author":"ZJ Sun","year":"2022","unstructured":"Sun ZJ et al (2022) A deep learning-based framework for automatic analysis of the nanoparticle morphology in SEM\/TEM images. Nanoscale 14(30):10761\u201310772. https:\/\/doi.org\/10.1039\/d2nr01029a","journal-title":"Nanoscale"},{"key":"9544_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/s18010156","author":"HG Li","year":"2018","unstructured":"Li HG et al (2018) Superpixel-based feature for aerial image scene recognition. Sensors. https:\/\/doi.org\/10.3390\/s18010156","journal-title":"Sensors"},{"issue":"3","key":"9544_CR27","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1109\/tmi.2015.2496296","volume":"35","author":"ZQ Tian","year":"2016","unstructured":"Tian ZQ et al (2016) Superpixel-based segmentation for 3d prostate MR images. IEEE Trans Med Imaging 35(3):791\u2013801. https:\/\/doi.org\/10.1109\/tmi.2015.2496296","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"9544_CR28","doi-asserted-by":"publisher","first-page":"166","DOI":"10.25165\/j.ijabe.20201304.5607","volume":"13","author":"QH Yang","year":"2020","unstructured":"Yang QH et al (2020) Superpixel-based segmentation algorithm for mature citrus. Int J Agric Biol Eng 13(4):166\u2013171. https:\/\/doi.org\/10.25165\/j.ijabe.20201304.5607","journal-title":"Int J Agric Biol Eng"},{"key":"9544_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2022.3212113","author":"YP Liu","year":"2022","unstructured":"Liu YP et al (2022) Layer segmentation of oct fingerprints with an adaptive gaussian prior guided transformer. IEEE Trans Instrum Measurement. https:\/\/doi.org\/10.1109\/tim.2022.3212113","journal-title":"IEEE Trans Instrum Measurement"},{"key":"9544_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s12046-018-0955-2","author":"A Sinduja","year":"2018","unstructured":"Sinduja A, Suruliandi A (2018) Block-based tri-channel hybrid segmentation of images for foreground extraction. Sadhana-Acad Proceed Eng Sci. https:\/\/doi.org\/10.1007\/s12046-018-0955-2","journal-title":"Sadhana-Acad Proceed Eng Sci"},{"key":"9544_CR31","doi-asserted-by":"publisher","unstructured":"Sheng H, et al. (2022) Foreign fibers detection using improved otsu-based maximum entropy algorithm in spinning process. In: 2022 6th International conference on robotics and automation sciences (ICRAS), pp. 206-210, https:\/\/doi.org\/10.1109\/ICRAS55217.2022.9842190","DOI":"10.1109\/ICRAS55217.2022.9842190"},{"issue":"3","key":"9544_CR32","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1166\/jmihi.2020.2970","volume":"10","author":"H Zhang","year":"2020","unstructured":"Zhang H, Zhang HJ (2020) A novel segmentation method for brain MRI using a block-based integrated fuzzy C-means clustering algorithm. J Med Imaging Health Inform 10(3):579\u2013585. https:\/\/doi.org\/10.1166\/jmihi.2020.2970","journal-title":"J Med Imaging Health Inform"},{"issue":"3\u20134","key":"9544_CR33","doi-asserted-by":"publisher","first-page":"2745","DOI":"10.1007\/s11042-019-08268-8","volume":"79","author":"A Kumar","year":"2020","unstructured":"Kumar A et al (2020) Semi-supervised Otsu based hyperbolic tangent gaussian kernel fuzzy C-mean clustering for dental radiographs segmentation. Multimed Tools Appl 79(3\u20134):2745\u20132768. https:\/\/doi.org\/10.1007\/s11042-019-08268-8","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"9544_CR34","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.3233\/xst-221245","volume":"30","author":"YS Malik","year":"2022","unstructured":"Malik YS et al (2022) Applying an adaptive Otsu-based initialization algorithm to optimize active contour models for skin lesion segmentation. J Xray Sci Technol 30(6):1169\u20131184. https:\/\/doi.org\/10.3233\/xst-221245","journal-title":"J Xray Sci Technol"},{"issue":"2","key":"9544_CR35","doi-asserted-by":"publisher","first-page":"2200170","DOI":"10.1002\/ppsc.202200170","volume":"40","author":"JJS Noval","year":"2023","unstructured":"Noval JJS, G\u00f3mez\u2010Merch\u00e1n R, Le\u00f1ero\u2010Bardallo JA, Gontard LC (2023) TEMAS: a flexible non\u2010ai algorithm for metrology of single\u2010core and core\u2010shell nanoparticles from TEM images. Part Part Syst Charact 40(2):2200170. https:\/\/doi.org\/10.1002\/ppsc.202200170","journal-title":"Part Part Syst Charact"},{"key":"9544_CR36","unstructured":"He KM, et al. (2018) Mask R-CNN. arXiv:1703.06870v3 [cs.CV] 24 Jan"},{"key":"9544_CR37","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1007\/s11760-020-01779-0","volume":"15","author":"F Zhang","year":"2021","unstructured":"Zhang F et al (2021) Rodlike nanoparticle parameter measurement method based on improved Mask R-CNN segmentation. Signal Image Video Process 15:579\u2013587. https:\/\/doi.org\/10.1007\/s11760-020-01779-0","journal-title":"Signal Image Video Process"},{"key":"9544_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101845","author":"D Loh","year":"2021","unstructured":"Loh D et al (2021) A deep learning approach to the screening of malaria infection: automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN. IEEE Trans Nanotechnol. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101845","journal-title":"IEEE Trans Nanotechnol"},{"key":"9544_CR39","unstructured":"Zhou X et al. (2019) Objects as Points. arXiv:1904.07850v2 [cs.CV] 25 Apr"},{"key":"9544_CR40","doi-asserted-by":"crossref","unstructured":"Lon J et al. (2015) Fully convolutional networks for semantic segmentation. arXiv:1411.4038v2 [cs.CV] 8 Mar","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"1","key":"9544_CR41","doi-asserted-by":"publisher","first-page":"39","DOI":"10.18280\/ts.380104","volume":"38","author":"Z Al-Ameen","year":"2021","unstructured":"Al-Ameen Z (2021) Contrast enhancement of digital images using an improved type-II fuzzy set-based algorithm. Traitement Du Signal 38(1):39\u201350. https:\/\/doi.org\/10.18280\/ts.380104","journal-title":"Traitement Du Signal"},{"issue":"10","key":"9544_CR42","doi-asserted-by":"publisher","first-page":"4949","DOI":"10.1007\/s00500-021-06539-x","volume":"26","author":"JR Jebadass","year":"2022","unstructured":"Jebadass JR, Balasubramaniam P (2022) Low contrast enhancement technique for color images using interval-valued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization. Soft Comput 26(10):4949\u20134960. https:\/\/doi.org\/10.1007\/s00500-021-06539-x","journal-title":"Soft Comput"},{"key":"9544_CR43","doi-asserted-by":"crossref","unstructured":"Liu S et al. (2018) Path aggregation network for instance segmentation. arXiv:1803.01534v4 [cs.CV] 18 Sep","DOI":"10.1109\/CVPR.2018.00913"},{"key":"9544_CR44","unstructured":"Liu S et al. (2019) Learning spatial fusion for single-shot object detection. arXiv:1911.09516v2 [cs.CV] 25 Nov"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09544-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09544-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09544-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T15:27:07Z","timestamp":1713281227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09544-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":44,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9544"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09544-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,3,1]]},"assertion":[{"value":"31 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}