{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:44:02Z","timestamp":1780544642002,"version":"3.54.1"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1016\/j.cmpb.2022.107031","type":"journal-article","created":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T17:15:56Z","timestamp":1658078156000},"page":"107031","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":28,"special_numbering":"C","title":["KUB-UNet: Segmentation of Organs of Urinary System from a KUB X-ray Image"],"prefix":"10.1016","volume":"224","author":[{"given":"Geeta","family":"Rani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priyam","family":"Thakkar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshat","family":"Verma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanshika","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rugved","family":"Chavan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijaypal Singh","family":"Dhaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravindra Kumar","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugenio","family":"Vocaturo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ester","family":"Zumpano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2022.107031_bib0001","unstructured":"Chronic Kidney Disease - World Kidney Day, (n.d.). https:\/\/www.worldkidneyday.org\/facts\/chronic-kidney-disease\/ (accessed March 17, 2022)."},{"key":"10.1016\/j.cmpb.2022.107031_bib0002","unstructured":"Urinary Disorders - Symptoms, Causes, Treatments, (n.d.). https:\/\/www.healthgrades.com\/right-care\/kidneys-and-the-urinary-system\/urinary-disorders (accessed February 3, 2022)."},{"key":"10.1016\/j.cmpb.2022.107031_bib0003","doi-asserted-by":"crossref","unstructured":"A.P.F. Turner, Perspective-An Age of Sensors, (2022). doi:10.1149\/2754-2726\/ac5523.","DOI":"10.1149\/2754-2726\/ac5523"},{"key":"10.1016\/j.cmpb.2022.107031_bib0004","doi-asserted-by":"crossref","DOI":"10.1149\/2754-2726\/ac5ddd","article-title":"Perspective\u2014Challenges in Biomedical Analysis: From Classical Sensors to Stochastic Sensors","volume":"1","author":"Stefan-van Staden","year":"2022","journal-title":"ECS Sensors Plus"},{"key":"10.1016\/j.cmpb.2022.107031_bib0005","doi-asserted-by":"crossref","unstructured":"V. Singh Dhaka, G. Rani, Transforming view of medical images using deep learning, (2020). doi:10.1007\/s00521-020-04857-z.","DOI":"10.1007\/s00521-020-04857-z"},{"key":"10.1016\/j.cmpb.2022.107031_bib0006","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/nrurol.2016.154","article-title":"An overview of kidney stone imaging techniques","volume":"13","author":"Brisbane","year":"2016","journal-title":"Nat Rev Urol"},{"key":"10.1016\/j.cmpb.2022.107031_bib0007","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1002\/ima.22544","article-title":"A deep learning model for mass screening of COVID-19","volume":"31","author":"Dhaka","year":"2021","journal-title":"International Journal of Imaging Systems and Technology"},{"key":"10.1016\/j.cmpb.2022.107031_bib0008","doi-asserted-by":"crossref","DOI":"10.1088\/1757-899X\/1099\/1\/012068","article-title":"Efficient Predictive Modelling for Classification of Coronary Artery Diseases Using Machine Learning Approach","volume":"1099","author":"Savita","year":"2021","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"10.1016\/j.cmpb.2022.107031_bib0009","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/978-981-13-8406-6_72","article-title":"Real-Time Object Detection and Tracking Using Velocity Control","volume":"141","author":"Rani","year":"2020","journal-title":"Smart Innovation, Systems and Technologies"},{"issue":"21","key":"10.1016\/j.cmpb.2022.107031_bib0010","first-page":"5386","article-title":"IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet","volume":"21","author":"Kundu","year":"2021","journal-title":"Sensors 2021"},{"key":"10.1016\/j.cmpb.2022.107031_bib0011","first-page":"1","article-title":"Applying deep learning-based multi-modal for detection of coronavirus","volume":"1","author":"Rani","year":"2021","journal-title":"Multimedia Systems"},{"issue":"18","key":"10.1016\/j.cmpb.2022.107031_bib0012","doi-asserted-by":"crossref","first-page":"15043","DOI":"10.1007\/s00521-020-04857-z","article-title":"Transforming view of medical images using deep learning","volume":"32","author":"Pradhan","year":"2020","journal-title":"Neural Computing and Applications 2020"},{"key":"10.1016\/j.cmpb.2022.107031_bib0013","first-page":"1","article-title":"Machine Learning Model for Multi-View Visualization of Medical Images","volume":"64","author":"Pradhan","year":"2021","journal-title":"The Computer Journal"},{"key":"10.1016\/j.cmpb.2022.107031_bib0014","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1002\/ima.22408","article-title":"Optimized contrast enhancement for tumor detection","volume":"30","author":"Agarwal","year":"2020","journal-title":"International Journal of Imaging Systems and Technology"},{"key":"10.1016\/j.cmpb.2022.107031_bib0015","doi-asserted-by":"crossref","unstructured":"G. Rani, M. Agarwal, Contrast Enhancement Using Optimum Threshold Selection, International Journal of Software Innovation. 8 (n.d.). doi:10.4018\/IJSI.2020070107.","DOI":"10.4018\/IJSI.2020070107"},{"key":"10.1016\/j.cmpb.2022.107031_bib0016","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"10.1016\/j.cmpb.2022.107031_bib0017","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/B978-0-12-819061-6.00014-8","article-title":"Diabetes prediction using artificial neural network","author":"Pradhan","year":"2020","journal-title":"Deep Learning Techniques for Biomedical and Health Informatics"},{"key":"10.1016\/j.cmpb.2022.107031_bib0018","doi-asserted-by":"crossref","unstructured":"M.G. Oza, G. Rani, V.S. Dhaka, Glaucoma Detection Using Convolutional Neural Networks, in: 2021: pp. 1\u20137. doi:10.4018\/978-1-7998-2742-9.ch001.","DOI":"10.4018\/978-1-7998-2742-9.ch001"},{"key":"10.1016\/j.cmpb.2022.107031_bib0019","unstructured":"G. Rani, P.K. Tiwari, IGI Global, Handbook of research on disease prediction through data analytics and machine learning, (n.d.) 586."},{"key":"10.1016\/j.cmpb.2022.107031_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13673-020-00220-2","article-title":"Generalization of intensity distribution of medical images using GANs","volume":"10","author":"Lee","year":"2020","journal-title":"Human-Centric Computing and Information Sciences"},{"key":"10.1016\/j.cmpb.2022.107031_bib0021","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.cmpb.2022.107031_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.postharvbio.2021.111808","article-title":"Apple stem\/calyx real-time recognition using YOLO-v5 algorithm for fruit automatic loading system","volume":"185","author":"Wang","year":"2022","journal-title":"Postharvest Biology and Technology"},{"key":"10.1016\/j.cmpb.2022.107031_bib0023","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 Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.cmpb.2022.107031_bib0024","first-page":"3","article-title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","volume":"11045","author":"Zhou","year":"2018","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"10.1016\/j.cmpb.2022.107031_bib0025","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"10.1016\/j.cmpb.2022.107031_bib0026","unstructured":"O. Oktay, J. Schlemper, L. le Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y. Hammerla, B. Kainz, B. Glocker, D. Rueckert, Attention U-Net: Learning Where to Look for the Pancreas, (2018). https:\/\/arxiv.org\/abs\/1804.03999v3 (accessed February 21, 2022)."},{"key":"10.1016\/j.cmpb.2022.107031_bib0027","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1002\/mp.13438","article-title":"U-Net based deep learning bladder segmentation in CT urography","volume":"46","author":"Ma","year":"2019","journal-title":"Med Phys"},{"key":"10.1016\/j.cmpb.2022.107031_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103906","article-title":"Kidney segmentation from computed tomography images using deep neural network","volume":"123","author":"da Cruz","year":"2020","journal-title":"Computers in Biology and Medicine"},{"issue":"8","key":"10.1016\/j.cmpb.2022.107031_bib0029","first-page":"1772","article-title":"Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model","volume":"8","author":"T\u00fcrk","year":"2020","journal-title":"Mathematics 2020"},{"key":"10.1016\/j.cmpb.2022.107031_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2020.100357","article-title":"3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net","volume":"19","author":"Zhao","year":"2020","journal-title":"Informatics in Medicine Unlocked"},{"key":"10.1016\/j.cmpb.2022.107031_bib0031","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1007\/s11548-018-1856-x","article-title":"FCN-based approach for the automatic segmentation of bone surfaces in ultrasound images","volume":"13","author":"Villa","year":"2018","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10.1016\/j.cmpb.2022.107031_bib0032","unstructured":"The VIA Annotation Software for Images, Audio and Video | Proceedings of the 27th ACM International Conference on Multimedia, (n.d.). https:\/\/dl.acm.org\/doi\/10.1145\/3343031.3350535 (accessed February 3, 2022)."},{"key":"10.1016\/j.cmpb.2022.107031_bib0033","unstructured":"Mobilefish.com - Record XY mouse coordinates on an uploaded image, (n.d.). https:\/\/www.mobilefish.com\/services\/record_mouse_coordinates\/record_mouse_coordinates.php (accessed February 21, 2022)."},{"key":"10.1016\/j.cmpb.2022.107031_bib0034","unstructured":"R. Kidambi, P. Netrapalli, P. Jain, S.M. Kakade, On the insufficiency of existing momentum schemes for Stochastic Optimization, 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings. (2018). https:\/\/arxiv.org\/abs\/1803.05591v2 (accessed February 21, 2022)."},{"key":"10.1016\/j.cmpb.2022.107031_bib0035","unstructured":"D.P. Kingma, J.L. Ba, Adam: A Method for Stochastic Optimization, 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. (2014). https:\/\/arxiv.org\/abs\/1412.6980v9 (accessed February 21, 2022)."},{"key":"10.1016\/j.cmpb.2022.107031_bib0036","doi-asserted-by":"crossref","unstructured":"J. Bertels, T. Eelbode, M. Berman, D. Vandermeulen, F. Maes, R. Bisschops, M. Blaschko, Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11765 LNCS (2019) 92\u2013100. doi:10.1007\/978-3-030-32245-8_11.","DOI":"10.1007\/978-3-030-32245-8_11"},{"key":"10.1016\/j.cmpb.2022.107031_bib0037","unstructured":"GitHub - tzutalin\/labelImg: LabelImg is a graphical image annotation tool and label object bounding boxes in images, (n.d.). https:\/\/github.com\/tzutalin\/labelImg (accessed February 3, 2022)."},{"key":"10.1016\/j.cmpb.2022.107031_bib0038","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1023\/B:VLSI.0000028532.53893.82","article-title":"Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement","volume":"38","author":"Reza","year":"2022","journal-title":"The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology"},{"key":"10.1016\/j.cmpb.2022.107031_bib0039","unstructured":"ad M. Abu-Faraj, M. Zubi, Analysis and implementation of kidney stones detection by applying segmentation techniques on computerized tomography scans, ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS-N. 43 (n.d.) 590\u2013602."},{"key":"10.1016\/j.cmpb.2022.107031_bib0040","series-title":"2020 IEEE 5th International Conference on Signal and Image Processing","first-page":"118","article-title":"Urinary Stones Segmentation in Abdominal X-Ray Images Based on U-Net Deep Learning Model and Data Augmentation Techniques","volume":"2020","author":"Preedanan","year":"2020"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260722004138?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260722004138?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:14:12Z","timestamp":1759205652000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260722004138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":40,"alternative-id":["S0169260722004138"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2022.107031","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2022,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"KUB-UNet: Segmentation of Organs of Urinary System from a KUB X-ray Image","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2022.107031","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107031"}}