{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:29:00Z","timestamp":1779384540133,"version":"3.53.1"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,16]],"date-time":"2018-01-16T00:00:00Z","timestamp":1516060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union's FP7 programme","award":["612471"],"award-info":[{"award-number":["612471"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI), showing robustness while reducing false positive and false negative detections.<\/jats:p>","DOI":"10.3390\/jimaging4010020","type":"journal-article","created":{"date-parts":[[2018,1,17]],"date-time":"2018-01-17T04:23:44Z","timestamp":1516163024000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Glomerulus Classification and Detection Based on Convolutional Neural Networks"],"prefix":"10.3390","volume":"4","author":[{"given":"Jaime","family":"Gallego","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, University of Castilla La Mancha, Ciudad Real 13071, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7748-6756","authenticated-orcid":false,"given":"Anibal","family":"Pedraza","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Castilla La Mancha, Ciudad Real 13071, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1285-5810","authenticated-orcid":false,"given":"Samuel","family":"Lopez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Castilla La Mancha, Ciudad Real 13071, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Steiner","sequence":"additional","affiliation":[{"name":"TissueGnostics GmbH, Vienna 1020, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucia","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"Hospital General Universitario, Ciudad Real 13005, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arvydas","family":"Laurinavicius","sequence":"additional","affiliation":[{"name":"Vilnius University Hospital Santariskes Clinics and Vilnius University, Vilnius 08406, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7345-4869","authenticated-orcid":false,"given":"Gloria","family":"Bueno","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Castilla La Mancha, Ciudad Real 13071, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1159\/000443482","article-title":"New trends of emerging technologies in digital pathology","volume":"83","author":"Bueno","year":"2016","journal-title":"Pathobiology"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Janowczyk, A., and Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J. Pathol. Inform., 7.","DOI":"10.4103\/2153-3539.186902"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: Challenges and opportunities","volume":"33","author":"Madabhushi","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1093\/ndt\/gft480","article-title":"Hypertension, glomerular hypertrophy and nephrosclerosis: The effect of race","volume":"29","author":"Hughson","year":"2014","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1111\/j.1600-0463.2005.apm_587.x","article-title":"Glomerular structural changes in pregnant, diabetic, and pregnant\u2014Diabetic rats","volume":"113","author":"Rasch","year":"2005","journal-title":"Apmis"},{"key":"ref_6","unstructured":"Ma, J., Jun, Z., and Jinglu, H. (2009, January 18\u201321). Glomerulus extraction by using genetic algorithm for edge patching. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway."},{"key":"ref_7","first-page":"20","article-title":"Automated quantitative image analysis of glomerular desmin immunostaining as a sensitive injury marker in spontaneously diabetic torii rats","volume":"1","author":"Hirohashi","year":"2014","journal-title":"J. Biomed. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.etp.2014.11.007","article-title":"Quantitative analysis of markers of podocyte injury in the rat puromycin aminonucleoside nephropathy model","volume":"67","author":"Kakimoto","year":"2015","journal-title":"Exp. Toxicol. Pathol."},{"key":"ref_9","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kato, T., Relator, R., Ngouv, H., Hirohashi, Y., Takaki, O., Kakimoto, T., and Okada, K. (2015). Segmental HOG: New descriptor for glomerulus detection in kidney microscopy image. BMC Bioinform., 16.","DOI":"10.1186\/s12859-015-0739-1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 27\u201329). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.cmpb.2015.10.023","article-title":"Measurement of glomerulus diameter and Bowman\u2019s space width of renal albino rats","volume":"126","author":"Kotyk","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/0020-0190(72)90045-2","article-title":"An efficient algorithm for determining the convex hull of a finite planar set","volume":"1","author":"Graham","year":"1972","journal-title":"Inf. Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mar\u00e9e, R., Dallongeville, S., Olivo-Marin, J.C., and Meas-Yedid, V. (2016, January 13\u201316). An approach for detection of glomeruli in multisite digital pathology. Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic.","DOI":"10.1109\/ISBI.2016.7493442"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"021102","DOI":"10.1117\/1.JMI.4.2.021102","article-title":"Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology","volume":"4","author":"Ginley","year":"2017","journal-title":"J. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.kint.2017.01.011","article-title":"Computer-assisted topological analysis of renal allograft inflammation adds to risk evaluation at diagnosis of humoral rejection","volume":"92","author":"Sicard","year":"2017","journal-title":"Kidney Int."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Simon, O., Yacoub, R., Jain, S., and Sarder, P. (arXiv, 2017). Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images, arXiv.","DOI":"10.1038\/s41598-018-20453-7"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, M., Wu, T., and Bennett, K.M. (2015, January 21\u201326). A novel Hessian based algorithm for rat kidney glomerulus detection in 3D MRI. Proceedings of the SPIE Medical Imaging. International Society for Optics and Photonics, Orlando, FL, USA.","DOI":"10.1117\/12.2081484"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1136\/amiajnl-2012-001540","article-title":"Pathology imaging informatics for quantitative analysis of whole-slide images","volume":"20","author":"Kothari","year":"2013","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"034003","DOI":"10.1117\/1.JMI.1.3.034003","article-title":"Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features","volume":"1","author":"Wang","year":"2014","journal-title":"J. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9","DOI":"10.4103\/2153-3539.112694","article-title":"Classification of mitotic figures with convolutional neural networks and seeded blob features","volume":"4","author":"Malon","year":"2013","journal-title":"J. Pathol. Inform."},{"key":"ref_22","first-page":"633","article-title":"Automated Detection of Diabetic Retinopathy Using Deep Convolutional Neural Networks","volume":"3","author":"Xu","year":"2016","journal-title":"Med. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain tumour segmentation with deep neural networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Temerinac-Ott, M., Forestier, G., Schmitz, J., Hermsen, M., Br\u00e4sen, J.H., Feuerhake, F., and Wemmert, C. (2017, January 18\u201320). Detection of glomeruli in renal pathology by mutual comparison of multiple staining modalities. Proceedings of the 2017 10th International Symposium on Image and Signal Processing and Analysis (ISPA), Ljubljana, Slovenia.","DOI":"10.1109\/ISPA.2017.8073562"},{"key":"ref_25","unstructured":"Gadermayra, M., Dombrowskia, A.K., Klinkhammerb, B.M., Boorb, P., and Merhofa, D. (arXiv, 2016). CNN Cascades for Segmenting Whole Slide Images of the Kidney, arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"243","DOI":"10.4103\/0971-4065.114462","article-title":"Basics of kidney biopsy: A nephrologist\u2019s perspective","volume":"23","author":"Agarwal","year":"2013","journal-title":"Indian J. Nephrol."},{"key":"ref_27","unstructured":"AIDPATH (Academia and Industry for Digital Pathology) (2018, January 16). European Project FP7 612471. Kidney database. Available online: http:\/\/aidpath.eu\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1002\/path.4262","article-title":"A matrix approach to guide IHC-based tissue biomarker development in oncology drug discovery","volume":"232","author":"Smith","year":"2014","journal-title":"J. Pathol."},{"key":"ref_29","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Stateline, NV, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/38.946629","article-title":"Color transfer between images","volume":"21","author":"Reinhard","year":"2001","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_33","unstructured":"Gallego, J., Swiderska-Chadaj, Z., Deniz, O., and Bueno, G. (December, January 29). Ki67 hot-spots detection on histopathological images of breast carcinoma using convolutional neural networks. Proceedings of the Anual Congress of the Biomedical Engineering Spanish Society, Lejona, Spain."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"498746","DOI":"10.1155\/2015\/498746","article-title":"Comparison of the manual, semiautomatic, and automatic selection and leveling of hot spots in whole slide images for ki-67 quantification in meningiomas","volume":"2015","author":"Swiderska","year":"2015","journal-title":"Anal. Cell. Pathol."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/1\/20\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:51:25Z","timestamp":1760194285000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/1\/20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,16]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["jimaging4010020"],"URL":"https:\/\/doi.org\/10.3390\/jimaging4010020","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,16]]}}}