{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T04:32:04Z","timestamp":1749184324630,"version":"3.38.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIC"],"published-print":{"date-parts":[[2022,3,18]]},"abstract":"<jats:p>In the diagnosis of chronic kidney disease, glomerulus as the blood filter provides important information for an accurate disease diagnosis. Thus automatic localization of the glomeruli is the necessary groundwork for future auxiliary kidney disease diagnosis, such as glomerular classification and area measurement. In this paper, we propose an efficient glomerular object locator in kidney whole slide image(WSI) based on proposal-free network and dynamic scale evaluation method. In the training phase, we construct an intensive proposal-free network which can learn efficiently the fine-grained features of the glomerulus. In the evaluation phase, a dynamic scale evaluation method is utilized to help the well-trained model find the most appropriate evaluation scale for each high-resolution WSI. We collect and digitalize 1204 renal biopsy microscope slides containing more than 41000 annotated glomeruli, which is the largest number of dataset to our best knowledge. We validate the each component of the proposed locator via the ablation study. Experimental results confirm that the proposed locator outperforms recently proposed approaches and pathologists by comparing F 1 and run time in localizing glomeruli from WSIs at a resolution of 0.25\u00a0\u03bcm\/pixel and thus achieves state-of-the-art performance. Particularly, the proposed locator can be embedded into the renal intelligent auxiliary diagnosis system for renal clinical diagnosis by localizing glomeruli in high-resolution WSIs effectively.<\/jats:p>","DOI":"10.3233\/aic-210073","type":"journal-article","created":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T16:23:34Z","timestamp":1630081414000},"page":"245-258","source":"Crossref","is-referenced-by-count":4,"title":["An efficient glomerular object locator for renal whole slide images using proposal-free network and dynamic scale evaluation method"],"prefix":"10.1177","volume":"34","author":[{"given":"Xueyu","family":"Liu","sequence":"first","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:\u00a0liming01@tyut.edu.cn"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:\u00a0liming01@tyut.edu.cn"}]},{"given":"Yongfei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:\u00a0liming01@tyut.edu.cn"},{"name":"Faculty of Science and Technology, University of Macau, Taipa, Macau, China. E-mail:\u00a0yongfeiwu522@sina.com"}]},{"given":"Yilin","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:\u00a0liming01@tyut.edu.cn"}]},{"given":"Fang","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:\u00a0liming01@tyut.edu.cn"}]},{"given":"Daoxiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:\u00a0liming01@tyut.edu.cn"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China. E-mail:\u00a0wangchen8877322@163.com"}]},{"given":"Chuanfeng","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:\u00a0liming01@tyut.edu.cn"}]},{"given":"Guangze","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:\u00a0liming01@tyut.edu.cn"}]},{"given":"Xiaoshuang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Shanxi Provincial People\u2019s Hospital, Taiyuan, Shanxi, China. E-mail:\u00a0xiaoshuangzhou66@163.com"}]}],"member":"179","reference":[{"key":"10.3233\/AIC-210073_ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105273"},{"issue":"8","key":"10.3233\/AIC-210073_ref2","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1681\/ASN.2017111210","article-title":"Region-based convolutional neural nets for localization of glomeruli in trichrome-stained whole kidney sections","volume":"29","author":"Bukowy","year":"2018","journal-title":"Journal of the American Society of Nephrology"},{"issue":"6","key":"10.3233\/AIC-210073_ref3","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1001\/jama.2017.7797","article-title":"Unintended consequences of machine learning in medicine","volume":"318","author":"Cabitza","year":"2017","journal-title":"Jama"},{"issue":"10","key":"10.3233\/AIC-210073_ref4","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nature medicine"},{"key":"10.3233\/AIC-210073_ref5","doi-asserted-by":"crossref","unstructured":"N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), Vol. 1, IEEE, 2005, pp. 886\u2013893.","DOI":"10.1109\/CVPR.2005.177"},{"key":"10.3233\/AIC-210073_ref6","doi-asserted-by":"publisher","first-page":"23","DOI":"10.2147\/PLMI.S59826","article-title":"Whole slide imaging in pathology: Advantages, limitations, and emerging perspectives","volume":"7","author":"Farahani","year":"2015","journal-title":"Pathology and Laboratory Medicine International"},{"key":"10.3233\/AIC-210073_ref7","doi-asserted-by":"crossref","unstructured":"A.B. Fogo, Renal Pathology, Pediatric Nephrology, 2009.","DOI":"10.1007\/978-3-540-76341-3_24"},{"key":"10.3233\/AIC-210073_ref8","unstructured":"J. George, S. Skaria, V. Varun et al., Using yolo based deep learning network for real time detection and localization of lung nodules from low dose ct scans, in: Medical Imaging 2018: Computer-Aided Diagnosis, Vol. 10575, International Society for Optics and Photonics, 2018, p. 105751I."},{"key":"10.3233\/AIC-210073_ref9","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.119005"},{"issue":"5","key":"10.3233\/AIC-210073_ref10","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1038\/ki.2013.439","article-title":"Donor kidney biopsies: Pathology matters, and so does the pathologist","volume":"85","author":"Haas","year":"2014","journal-title":"Kidney international"},{"issue":"9","key":"10.3233\/AIC-210073_ref11","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"10.3233\/AIC-210073_ref12","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.186902"},{"issue":"1","key":"10.3233\/AIC-210073_ref13","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1530\/JOE-14-0164","article-title":"Automated image analysis of a glomerular injury marker desmin in spontaneously diabetic torii rats treated with losartan","volume":"222","author":"Kakimoto","year":"2014","journal-title":"Journal of Endocrinology"},{"issue":"1","key":"10.3233\/AIC-210073_ref14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-014-0430-y","article-title":"Segmental hog: New descriptor for glomerulus detection in kidney microscopy image","volume":"16","author":"Kato","year":"2015","journal-title":"Bmc Bioinformatics"},{"key":"10.3233\/AIC-210073_ref15","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging4070091"},{"key":"10.3233\/AIC-210073_ref17","doi-asserted-by":"publisher","first-page":"e194337","DOI":"10.1001\/jamanetworkopen.2019.4337","article-title":"Trends in the us and Canadian pathologist workforces from 2007 to 2017","volume":"5","author":"Metter","year":"2019","journal-title":"JAMA network open 2"},{"key":"10.3233\/AIC-210073_ref18","doi-asserted-by":"crossref","unstructured":"J. Redmon, S. Divvala, R. Girshick and A. Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.3233\/AIC-210073_ref19","doi-asserted-by":"crossref","unstructured":"J. Redmon and A. Farhadi, Yolo9000: Better, faster, stronger, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7263\u20137271.","DOI":"10.1109\/CVPR.2017.690"},{"issue":"6","key":"10.3233\/AIC-210073_ref21","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"10.3233\/AIC-210073_ref22","doi-asserted-by":"crossref","unstructured":"H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid and S. Savarese, Generalized intersection over union: A metric and a loss for bounding box regression, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 658\u2013666.","DOI":"10.1109\/CVPR.2019.00075"},{"issue":"1\u20133","key":"10.3233\/AIC-210073_ref23","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","article-title":"Labelme: A database and web-based tool for image annotation","volume":"77","author":"Russell","year":"2008","journal-title":"International journal of computer vision"},{"issue":"2","key":"10.3233\/AIC-210073_ref24","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1109\/TPAMI.2019.2922181","article-title":"Object detection from scratch with deep supervision","volume":"42","author":"Shen","year":"2019","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"issue":"1","key":"10.3233\/AIC-210073_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-20453-7","article-title":"Multi-radial lbp features as a tool for rapid glomerular detection and assessment in whole slide histopathology images","volume":"8","author":"Simon","year":"2018","journal-title":"Scientific reports"},{"issue":"3","key":"10.3233\/AIC-210073_ref26","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural processing letters"},{"key":"10.3233\/AIC-210073_ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967274"},{"issue":"2","key":"10.3233\/AIC-210073_ref28","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1109\/TIP.2009.2035882","article-title":"Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor","volume":"19","author":"Zhang","year":"2009","journal-title":"IEEE transactions on image processing"},{"key":"10.3233\/AIC-210073_ref29","doi-asserted-by":"crossref","unstructured":"Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye and D. Ren, Distance-iou loss: Faster and better learning for bounding box regression, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 12993\u201313000.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"10.3233\/AIC-210073_ref30","unstructured":"H.U. Zollinger and M.J. Mihatsch, Renal Pathology in Biopsy: Light, Electron and Immunofluorescent Microscopy and Clinical Aspects, Springer Science & Business Media, 2012."}],"container-title":["AI Communications"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/AIC-210073","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T03:47:51Z","timestamp":1741664871000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/AIC-210073"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":28,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/aic-210073","relation":{},"ISSN":["1875-8452","0921-7126"],"issn-type":[{"type":"electronic","value":"1875-8452"},{"type":"print","value":"0921-7126"}],"subject":[],"published":{"date-parts":[[2022,3,18]]}}}