{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T15:17:48Z","timestamp":1763219868236,"version":"3.45.0"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000848","name":"University of Edinburgh","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000848","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009147","name":"College of Medicine and Veterinary Medicine, University of Edinburgh","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009147","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S025987\/1"],"award-info":[{"award-number":["EP\/S025987\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.compbiomed.2025.110678","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T12:45:11Z","timestamp":1753274711000},"page":"110678","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["EmiNet: Moving bacteria detection on optical endomicroscopy images trained on synthetic data"],"prefix":"10.1016","volume":"196","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4410-4587","authenticated-orcid":false,"given":"Mehmet","family":"Demirel","sequence":"first","affiliation":[]},{"given":"Bethany","family":"Mills","sequence":"additional","affiliation":[]},{"given":"Erin","family":"Gaughan","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"Dhaliwal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3029-2425","authenticated-orcid":false,"given":"James R.","family":"Hopgood","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.compbiomed.2025.110678_b1","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-44145-y","article-title":"Clinical features for diagnosis of pneumonia among adults in primary care setting: A systematic and meta-review","volume":"9","author":"Htun","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2025.110678_b2","doi-asserted-by":"crossref","first-page":"S17","DOI":"10.1016\/S0140-6736(16)00404-9","article-title":"Structural modifications of the antimicrobial peptide ubiquicidin for pulmonary imaging of bacteria in the alveolar space","volume":"387","author":"Akram","year":"2016","journal-title":"Lancet"},{"issue":"6","key":"10.1016\/j.compbiomed.2025.110678_b3","doi-asserted-by":"crossref","first-page":"417","DOI":"10.3390\/diagnostics10060417","article-title":"Efficient pneumonia detection in chest xray images using deep transfer learning","volume":"10","author":"Hashmi","year":"2020","journal-title":"Diagnostics"},{"issue":"12","key":"10.1016\/j.compbiomed.2025.110678_b4","doi-asserted-by":"crossref","first-page":"6971","DOI":"10.1039\/C5SC00960J","article-title":"A labelled-ubiquicidin antimicrobial peptide for immediate in situ optical detection of live bacteria in human alveolar lung tissue","volume":"6","author":"Akram","year":"2015","journal-title":"Chem. Sci."},{"issue":"Supplement_2","key":"10.1016\/j.compbiomed.2025.110678_b5","doi-asserted-by":"crossref","first-page":"S27","DOI":"10.1086\/511159","article-title":"Infectious diseases society of america\/American thoracic society consensus guidelines on the management of community-acquired pneumonia in adults","volume":"44","author":"Mandell","year":"2007","journal-title":"Clin. Infect. Dis."},{"issue":"22","key":"10.1016\/j.compbiomed.2025.110678_b6","doi-asserted-by":"crossref","first-page":"5808","DOI":"10.3390\/ijms20225808","article-title":"Radiochemical approaches to imaging bacterial infections: Intracellular versus extracellular targets","volume":"20","author":"Northrup","year":"2019","journal-title":"Int. J. Mol. Sci."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.110678_b7","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1183\/09031936.00062512","article-title":"Confocal laser endomicroscopy for diagnosing lung cancer in vivo","volume":"41","author":"Fuchs","year":"2012","journal-title":"Eur. Respir. J."},{"key":"10.1016\/j.compbiomed.2025.110678_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101620","article-title":"Image computing for fibre-bundle endomicroscopy: A review","volume":"62","author":"Perperidis","year":"2020","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.110678_b9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1164\/rccm.200605-684OC","article-title":"In vivo imaging of the bronchial wall microstructure using fibered confocal fluorescence microscopy","volume":"175","author":"Thiberville","year":"2007","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"10.1016\/j.compbiomed.2025.110678_b10","series-title":"Medical Imaging 2021: Computer-Aided Diagnosis","article-title":"Fluorescence lifetime imaging endomicroscopy based ex-vivo lung cancer prediction using multi-scale concatenated-dilation convolutional neural networks","author":"Wang","year":"2021"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.110678_b11","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rmed.2011.09.009","article-title":"Imaging parenchymal lung diseases with confocal endomicroscopy","volume":"106","author":"Newton","year":"2012","journal-title":"Respir. Med."},{"issue":"2","key":"10.1016\/j.compbiomed.2025.110678_b12","doi-asserted-by":"crossref","DOI":"10.1088\/2050-6120\/ad12f7","article-title":"Applications of machine learning in time-domain fluorescence lifetime imaging: a review","volume":"12","author":"Gouzou","year":"2024","journal-title":"Methods Appl. Fluoresc."},{"key":"10.1016\/j.compbiomed.2025.110678_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101620","article-title":"Image computing for fibre-bundle endomicroscopy: A review","volume":"62","author":"Perperidis","year":"2020","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.compbiomed.2025.110678_b14","doi-asserted-by":"crossref","first-page":"2740","DOI":"10.1109\/TMI.2023.3264433","article-title":"MISSU: 3D medical image segmentation via self-distilling TransUNet","volume":"42","author":"Wang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"year":"2021","author":"Chen","series-title":"TransUNet: Transformers make strong encoders for medical image segmentation","key":"10.1016\/j.compbiomed.2025.110678_b15"},{"year":"2021","author":"Wang","series-title":"TransBTS: Multimodal brain tumor segmentation using transformer","key":"10.1016\/j.compbiomed.2025.110678_b16"},{"issue":"10","key":"10.1016\/j.compbiomed.2025.110678_b17","doi-asserted-by":"crossref","first-page":"2808","DOI":"10.1109\/TMI.2021.3066161","article-title":"Label-free segmentation of COVID-19 lesions in lung CT","volume":"40","author":"Yao","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2025.110678_b18","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","first-page":"162","article-title":"A robust volumetric transformer for accurate 3D tumor segmentation","author":"Peiris","year":"2022"},{"year":"2023","author":"Hu","series-title":"Label-free liver tumor segmentation","key":"10.1016\/j.compbiomed.2025.110678_b19"},{"issue":"12","key":"10.1016\/j.compbiomed.2025.110678_b20","doi-asserted-by":"crossref","first-page":"3618","DOI":"10.1073\/pnas.1422953112","article-title":"Visual turing test for computer vision systems","volume":"112","author":"Geman","year":"2015","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.110678_b21","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1007\/s10278-023-00803-2","article-title":"An image turing test on realistic gastroscopy images generated by using the progressive growing of generative adversarial networks","volume":"36","author":"Shin","year":"2023","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.compbiomed.2025.110678_b22","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","first-page":"657","article-title":"Patch-based sparse representation for bacterial detection","author":"Eldaly","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.110678_b23","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2019.06.009","article-title":"Bayesian bacterial detection using irregularly sampled optical endomicroscopy images","volume":"57","author":"Eldaly","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.110678_b24","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1109\/TIP.2024.3361217","article-title":"Bayesian statistical analysis for bacterial detection in pulmonary endomicroscopic fluorescence lifetime imaging","volume":"33","author":"Demirel","year":"2024","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.110678_b25","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1109\/TMI.2024.3354408","article-title":"Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations","volume":"43","author":"Kreitner","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"year":"2015","author":"Ronneberger","series-title":"U-net: Convolutional networks for biomedical image segmentation","key":"10.1016\/j.compbiomed.2025.110678_b26"},{"key":"10.1016\/j.compbiomed.2025.110678_b27","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Quo vadis, action recognition? A new model and the kinetics dataset","author":"Carreira","year":"2017"},{"doi-asserted-by":"crossref","unstructured":"H. Lamdouar, W. Xie, A. Zisserman, Segmenting Invisible Moving Objects, in: British Machine Vision Conference, 2021.","key":"10.1016\/j.compbiomed.2025.110678_b28","DOI":"10.5244\/C.35.13"},{"unstructured":"G.-P. Ji, K. Fu, Z. Wu, D.-P. Fan, J. Shen, L. Shao, Full-Duplex Strategy for Video Object Segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2021, pp. 4922\u20134933.","key":"10.1016\/j.compbiomed.2025.110678_b29"},{"key":"10.1016\/j.compbiomed.2025.110678_b30","series-title":"2021 IEEE\/CVF International Conference on Computer Vision","first-page":"1544","article-title":"Learning motion-appearance co-attention for zero-shot video object segmentation","author":"Yang","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.110678_b31","doi-asserted-by":"crossref","first-page":"5909","DOI":"10.1109\/TIP.2023.3326395","article-title":"Hierarchical graph pattern understanding for zero-shot video object segmentation","volume":"32","author":"Pei","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.compbiomed.2025.110678_b32","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","first-page":"14","article-title":"TransFuse: Fusing transformers and CNNs for medical image segmentation","author":"Zhang","year":"2021"},{"year":"2020","author":"Dosovitskiy","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","key":"10.1016\/j.compbiomed.2025.110678_b33"},{"key":"10.1016\/j.compbiomed.2025.110678_b34","series-title":"Advances in Neural Information Processing Systems","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.110678_b35","first-page":"1","article-title":"Recurrent adaptive graph reasoning Network With Region and boundary interaction for salient object detection in optical remote sensing images","volume":"62","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compbiomed.2025.110678_b36","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/TIP.2020.3042084","article-title":"Dense attention fluid network for salient object detection in optical remote sensing images","volume":"30","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"P. Tokmakov, K. Alahari, C. Schmid, Learning Video Object Segmentation With Visual Memory, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2017.","key":"10.1016\/j.compbiomed.2025.110678_b37","DOI":"10.1109\/ICCV.2017.480"},{"key":"10.1016\/j.compbiomed.2025.110678_b38","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"Non-local neural networks","author":"Wang","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.110678_b39","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1109\/TIP.2020.3026866","article-title":"Ellipse recovery from blurred binary images","volume":"30","author":"Zamani","year":"2021","journal-title":"IEEE Trans. Image Process."},{"issue":"464","key":"10.1016\/j.compbiomed.2025.110678_b40","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.aal0033","article-title":"In situ identification of gram-negative bacteria in human lungs using a topical fluorescent peptide targeting lipid a","volume":"10","author":"Akram","year":"2018","journal-title":"Sci. Transl. Med."},{"key":"10.1016\/j.compbiomed.2025.110678_b41","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2023.1100161","article-title":"Specific in situ immuno-imaging of pulmonary-resident memory lymphocytes in human lungs","volume":"14","author":"Humphries","year":"2023","journal-title":"Front. Immunol."},{"year":"1999","author":"Council","series-title":"Size Limits of Very Small Microorganisms: Proceedings of a Workshop","key":"10.1016\/j.compbiomed.2025.110678_b42"},{"doi-asserted-by":"crossref","unstructured":"A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, T. Brox, FlowNet: Learning Optical Flow With Convolutional Networks, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2015.","key":"10.1016\/j.compbiomed.2025.110678_b43","DOI":"10.1109\/ICCV.2015.316"},{"year":"2017","author":"Geron","series-title":"Hands-On Machine Learning with Scikit-Learn and TensorFlow","key":"10.1016\/j.compbiomed.2025.110678_b44"},{"key":"10.1016\/j.compbiomed.2025.110678_b45","series-title":"Image Analysis","first-page":"363","article-title":"Two-frame motion estimation based on polynomial expansion","author":"Farneb\u00e4ck","year":"2003"},{"year":"2021","author":"Hatamizadeh","series-title":"UNETR: Transformers for 3D medical image segmentation","key":"10.1016\/j.compbiomed.2025.110678_b46"},{"year":"2016","author":"Milletari","series-title":"V-Net: Fully convolutional neural networks for volumetric medical image segmentation","key":"10.1016\/j.compbiomed.2025.110678_b47"},{"key":"10.1016\/j.compbiomed.2025.110678_b48","article-title":"HmsU-Net: A hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation","volume":"170","author":"Fu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2025.110678_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108057","article-title":"MS-TCNet: An effective transformer\u2013CNN combined network using multi-scale feature learning for 3D medical image segmentation","volume":"170","author":"Ao","year":"2024","journal-title":"Comput. Biol. Med."},{"year":"2016","author":"\u00c7i\u00e7ek","series-title":"3D U-net: Learning dense volumetric segmentation from sparse annotation","key":"10.1016\/j.compbiomed.2025.110678_b50"},{"year":"2018","author":"Oktay","series-title":"Attention U-net: Learning where to look for the pancreas","key":"10.1016\/j.compbiomed.2025.110678_b51"},{"key":"10.1016\/j.compbiomed.2025.110678_b52","series-title":"Computer and Computing Technologies in Agriculture IV","first-page":"8","article-title":"A Laplacian of Gaussian-based approach for spot detection in two-dimensional gel electrophoresis images","author":"He","year":"2011"},{"issue":"11","key":"10.1016\/j.compbiomed.2025.110678_b53","doi-asserted-by":"crossref","first-page":"4104","DOI":"10.1118\/1.2358326","article-title":"Characterization of difference of Gaussian filters in the detection of mammographic regions","volume":"33","author":"Catarious","year":"2006","journal-title":"Med. Phys."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.110678_b54","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2105-11-373","article-title":"Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images","volume":"11","author":"Kimori","year":"2010","journal-title":"BMC Bioinform."},{"issue":"21","key":"10.1016\/j.compbiomed.2025.110678_b55","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.3390\/electronics11213549","article-title":"Infrared weak and small target detection based on top-hat filtering and multi-feature fuzzy decision-making","volume":"11","author":"Yang","year":"2022","journal-title":"Electronics"}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525010297?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525010297?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T15:15:50Z","timestamp":1763219750000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482525010297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":55,"alternative-id":["S0010482525010297"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.110678","relation":{},"ISSN":["0010-4825"],"issn-type":[{"type":"print","value":"0010-4825"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EmiNet: Moving bacteria detection on optical endomicroscopy images trained on synthetic data","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.110678","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"110678"}}