{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T18:40:05Z","timestamp":1733164805898,"version":"3.30.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"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":[[2025,2]]},"DOI":"10.1016\/j.bspc.2024.106979","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T12:35:19Z","timestamp":1728045319000},"page":"106979","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["Accurate measurement of key structures in CBD patients using deep learning"],"prefix":"10.1016","volume":"100","author":[{"given":"Zheng","family":"Wang","sequence":"first","affiliation":[]},{"given":"Kaibin","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Mingcai","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Lingqi","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Zhiyuan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Minghao","family":"Wu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.bspc.2024.106979_b0005","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/j.bpg.2006.05.009","article-title":"Gallstone disease: Epidemiology, pathogenesis, and classification of biliary stones (common bile duct and intrahepatic)","volume":"20","author":"Susumu","year":"2006","journal-title":"Best Pract. Res. Clin. Gastroenterol."},{"issue":"5","key":"10.1016\/j.bspc.2024.106979_b0010","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0016-5107(00)70285-9","article-title":"Grading ercps by degree of difficulty: a new concept to produce more meaningful outcome data","volume":"51","author":"Schutz","year":"2000","journal-title":"Gastrointest. Endosc."},{"issue":"6","key":"10.1016\/j.bspc.2024.106979_b0015","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1016\/j.gie.2007.04.033","article-title":"Factors influencing the technical difficulty of endoscopic clearance of bile duct stones","volume":"66","author":"Kim","year":"2007","journal-title":"Gastrointest. Endosc."},{"issue":"05","key":"10.1016\/j.bspc.2024.106979_b0020","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1055\/a-1244-5698","article-title":"Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study","volume":"53","author":"Huang","year":"2021","journal-title":"Endoscopy"},{"issue":"05","key":"10.1016\/j.bspc.2024.106979_b0025","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1055\/a-0862-0346","article-title":"Endoscopic management of common bile duct stones: European society of gastrointestinal endoscopy (esge) guideline","volume":"51","author":"Manes","year":"2019","journal-title":"Endoscopy"},{"issue":"6","key":"10.1016\/j.bspc.2024.106979_b0030","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/j.gie.2018.10.001","article-title":"Asge guideline on the role of endoscopy in the evaluation and management of choledocholithiasis","volume":"89","author":"Buxbaum","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"10.1016\/j.bspc.2024.106979_b0035","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1007\/s10439-019-02349-3","article-title":"An effective cnn method for fully automated segmenting subcutaneous and visceral adipose tissue on ct scans","volume":"48","author":"Wang","year":"2020","journal-title":"Ann. Biomed. Eng."},{"issue":"4","key":"10.1016\/j.bspc.2024.106979_b0040","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.gie.2020.04.071","article-title":"Deep learning\u2013based pancreas segmentation and station recognition system in eus: Development and validation of a useful training tool (with video)","volume":"92","author":"Zhang","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"10.1016\/j.bspc.2024.106979_b0045","first-page":"4","article-title":"An artificial intelligence difficulty scoring system for stone removal during ercp: a prospective validation","author":"Huang","year":"2022","journal-title":"Endoscopy"},{"key":"10.1016\/j.bspc.2024.106979_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119979","article-title":"Artificial intelligence-based detection and assessment of ascites on ct scans","volume":"224","author":"Wang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2024.106979_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116519","article-title":"Structure-aware deep learning for chronic middle ear disease","volume":"194","author":"Wang","year":"2022","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.bspc.2024.106979_b0060","first-page":"5052435","article-title":"Review of deep learning approaches for thyroid cancer diagnosis","volume":"2022","author":"Anari","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"10.1016\/j.bspc.2024.106979_b0065","first-page":"1","article-title":"ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries","author":"Ranjbarzadeh","year":"2023","journal-title":"Soft. Comput."},{"issue":"12","key":"10.1016\/j.bspc.2024.106979_b0070","doi-asserted-by":"crossref","first-page":"3532","DOI":"10.1111\/jgh.15569","article-title":"Efficacy of an artificial neural network algorithm based on thick-slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones","volume":"36","author":"Hou","year":"2021","journal-title":"J. Gastroenterol. Hepatol."},{"issue":"12","key":"10.1016\/j.bspc.2024.106979_b0075","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.1136\/gutjnl-2018-317366","article-title":"Randomised controlled trial of wisense, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy","volume":"68","author":"Wu","year":"2019","journal-title":"Gut"},{"issue":"4","key":"10.1016\/j.bspc.2024.106979_b0080","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S2468-1253(19)30413-3","article-title":"Detection of colorectal adenomas with a real-time computer-aided system (endoangel): a randomised controlled study","volume":"5","author":"Gong","year":"2020","journal-title":"Lancet Gastroenterol. Hepatol."},{"issue":"1","key":"10.1016\/j.bspc.2024.106979_b0085","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.14704\/WEB\/V19I1\/WEB19123","article-title":"Image segmentation based deep learning for biliary tree diagnosis","volume":"19","author":"Mohammad","year":"2022","journal-title":"Webology"},{"key":"10.1016\/j.bspc.2024.106979_b0090","first-page":"1","article-title":"Artificial intelligence-assisted visual sensing technology under duodenoscopy of gallbladder stones","volume":"2021","author":"Li","year":"2021","journal-title":"J. Sens."},{"key":"10.1016\/j.bspc.2024.106979_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2022.101431","article-title":"A novel machine learning model and a public online prediction platform for prediction of post-ERCP-cholecystitis (PEC)","volume":"48","author":"Zhang","year":"2022","journal-title":"EClinicalMedicine"},{"doi-asserted-by":"crossref","unstructured":"Salem, Nourah M., et al. \u201cMachine and deep learning identified metabolites and clinical features associated with gallstone disease.\u201d Computer Methods and Programs in Biomedicine Update 3 (2023): 100106.","key":"10.1016\/j.bspc.2024.106979_b0100","DOI":"10.1016\/j.cmpbup.2023.100106"},{"issue":"6","key":"10.1016\/j.bspc.2024.106979_b0105","doi-asserted-by":"crossref","first-page":"e0217647","DOI":"10.1371\/journal.pone.0217647","article-title":"A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images","volume":"14","author":"Pang","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2024.106979_b0110","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.amsu.2020.10.040","article-title":"The influence of stone size on spontaneous passage of common bile duct stones in patients with acute cholangitis: a retrospective cohort study","volume":"60","author":"Sanguanlosit","year":"2020","journal-title":"Ann. Med. Surg."},{"issue":"44","key":"10.1016\/j.bspc.2024.106979_b0115","doi-asserted-by":"crossref","first-page":"7597","DOI":"10.3748\/wjg.v27.i44.7597","article-title":"Endoscopic management of difficult common bile duct stones: where are we now? A comprehensive review","volume":"27","author":"Tringali","year":"2021","journal-title":"World J. Gastroenterol."},{"key":"10.1016\/j.bspc.2024.106979_b0120","first-page":"1","article-title":"Optimal treatment for concomitant gallbladder stones with common bile duct stones and predictors for recurrence of common bile duct stones","author":"Lee","year":"2022","journal-title":"Surg. Endosc."},{"issue":"5","key":"10.1016\/j.bspc.2024.106979_b0125","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1111\/den.14193","article-title":"Endoscopic ultrasound versus magnetic resonance cholangiopancreatography for the diagnosis of computed tomography-negative common bile duct stone: prospective randomized controlled trial","volume":"34","author":"Suzuki","year":"2022","journal-title":"Dig. Endosc."},{"doi-asserted-by":"crossref","unstructured":"Pencovich, Niv, et al. \u201cSerum amylase levels is a predictor for negative endoscopic retrograde cholangiopancreatography for suspected common bile duct stones.\u201d Surgical Laparosc. Endosc. Percutan. Techniques 31.5 (2021): 528-532.","key":"10.1016\/j.bspc.2024.106979_b0130","DOI":"10.1097\/SLE.0000000000000916"},{"issue":"45","key":"10.1016\/j.bspc.2024.106979_b0135","doi-asserted-by":"crossref","first-page":"17148","DOI":"10.3748\/wjg.v20.i45.17148","article-title":"Endoscopic papillary large balloon dilation for removal of bile duct stones","volume":"20","author":"Sakai","year":"2014","journal-title":"World J Gastroenterol: WJG"},{"issue":"2","key":"10.1016\/j.bspc.2024.106979_b0140","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1111\/den.12740","article-title":"Double guidewire endoscopic technique, a major evolution in endoscopic retrograde cholangiopancreatography: results of a retrospective study with historical controls comparing two therapeutic sequential strategies","volume":"29","author":"Laquiere","year":"2017","journal-title":"Dig. Endosc."},{"doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 2015, pp. 234\u2013241.","key":"10.1016\/j.bspc.2024.106979_b0145","DOI":"10.1007\/978-3-319-24574-4_28"},{"doi-asserted-by":"crossref","unstructured":"B. Koonce, B. Koonce, Efficientnet, Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization (2021) 109\u2013123.","key":"10.1016\/j.bspc.2024.106979_b0150","DOI":"10.1007\/978-1-4842-6168-2_10"},{"doi-asserted-by":"crossref","unstructured":"J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431\u20133440.","key":"10.1016\/j.bspc.2024.106979_b0155","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"12","key":"10.1016\/j.bspc.2024.106979_b0160","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"L.-C. Chen, G. Papandreou, F. Schroff, H. Adam, Rethinking atrous convolution for semantic image segmentation, arXiv preprint arXiv:1706.05587.","key":"10.1016\/j.bspc.2024.106979_b0165"},{"doi-asserted-by":"crossref","unstructured":"Karkehabadi, Ali, et al. \u201cOn the connection between saliency guided training and robustness in image classification.\u201d 2024 12th International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2024.","key":"10.1016\/j.bspc.2024.106979_b0170","DOI":"10.1109\/ICICIP60808.2024.10477811"},{"doi-asserted-by":"crossref","unstructured":"Karkehabadi, Ali, Houman Homayoun, and Avesta Sasan. \u201cSMOOT: Saliency guided mask optimized online training.\u201d 2024 IEEE 17th Dallas Circuits and Systems Conference (DCAS). IEEE, 2024.","key":"10.1016\/j.bspc.2024.106979_b0175","DOI":"10.1109\/DCAS61159.2024.10539909"},{"doi-asserted-by":"crossref","unstructured":"B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning deep features for discriminative localization, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2921\u20132929.","key":"10.1016\/j.bspc.2024.106979_b0180","DOI":"10.1109\/CVPR.2016.319"},{"doi-asserted-by":"crossref","unstructured":"R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 618\u2013626.","key":"10.1016\/j.bspc.2024.106979_b0185","DOI":"10.1109\/ICCV.2017.74"},{"issue":"34","key":"10.1016\/j.bspc.2024.106979_b0190","doi-asserted-by":"crossref","first-page":"9993","DOI":"10.3748\/wjg.v21.i34.9993","article-title":"Detection of gallbladder stones by dual-energy spectral computed tomography imaging","volume":"21","author":"Chen","year":"2015","journal-title":"World J. Gastroenterol: WJG"},{"issue":"4","key":"10.1016\/j.bspc.2024.106979_b0195","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.acra.2016.10.006","article-title":"Clinical application of dual-energy spectral computed tomography in detecting cholesterol gallstones from surrounding bile","volume":"24","author":"Yang","year":"2017","journal-title":"Acad. Radiol."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809424010371?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809424010371?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T17:59:13Z","timestamp":1733162353000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809424010371"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":39,"alternative-id":["S1746809424010371"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2024.106979","relation":{},"ISSN":["1746-8094"],"issn-type":[{"type":"print","value":"1746-8094"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Accurate measurement of key structures in CBD patients using deep learning","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2024.106979","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106979"}}