{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T19:16:46Z","timestamp":1783451806809,"version":"3.55.0"},"reference-count":40,"publisher":"Bioscientifica","issue":"2","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":97,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.bioscientifica.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                    <jats:p\/>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>Analysis of immunofluorescent images can be time-consuming and observer-dependant. We aimed to develop a standardised method for analysing immunofluorescent images of human and mouse testicular tissues using an existing software package. We used QuPath to train and apply an artificial neural network with multilayer perception (ANN-MLP) on images. We were able to automate the segmentation of cells in regions of interest (ROIs) using both StarDist (a convolution neural network) and Watershed image transformation. Segmented cells were classified using QuPath object classification system, which was successfully applied to a range of mouse and human interstitial, Sertoli and spermatogonial germ cell markers. We found that manual counting and classification of cells decoupled the relationship between tubular area and a number of SOX9+ (r2 = 0.26, P = 0.35) and MAGE-A+ (r2 = 0.26, P = 0.35) cells. However, automating the segmentation of ROIs and cell classification with simple macros yielded results that maintained the correlation between tubular area and number of SOX9+ (r2 = 0.56, P = 0.03) and MAGE-A+ (r2 = 0.93, P = 0.002) cells. In addition, we were able to export data into R\/RStudio allowing for the analysis of classification-specific parameters such as mitotic index and cellular organisation in different regions of the tubule. Importantly, the time taken per image using the automated method was significantly faster (6,247 vs three seconds; P &amp;lt; 0.001) at segmenting tubules and quantifying cells than previous manual annotation methods. We propose the use of this method for analysis and cell quantification in testicular tissues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Lay summary<\/jats:title>\n                    <jats:p>Machine learning (ML) is a type of algorithm that forms part of artificial intelligence (AI). ML is able to learn, detect and predict sequences and structures such as words in sentences or objects in images. ML combined with the ability of modern computers to process large quantities of data and perform repetitive tasks in an automated way makes it an attractive research tool. In particular, the analysis of images taken from a microscope of patient or experimental samples is one area in which ML can excel. We found that open-source software containing ML could be trained on as few as six images. Once trained, the machine learning algorithm could analyse an image in approximately one minute. The same image would take a skilled researcher nearly two hours to analyse. In addition to speed, ML was able to do this more accurately and consistently as well as being automated by a simple piece of code.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1530\/raf-25-0210","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T12:35:27Z","timestamp":1779107727000},"update-policy":"https:\/\/doi.org\/10.1530\/crossmarkpolicy-13","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning and automation methods for the segmentation, classification and quantification of testicular tissue sections"],"prefix":"10.1530","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-2363","authenticated-orcid":true,"given":"Adam J R","family":"Gadd","sequence":"first","affiliation":[{"name":"1"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iris","family":"Sanou","sequence":"additional","affiliation":[{"name":"2"},{"name":"3"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleanor","family":"Brain","sequence":"additional","affiliation":[{"name":"4"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jill","family":"Davies","sequence":"additional","affiliation":[{"name":"5"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0508-9585","authenticated-orcid":false,"given":"Adomas","family":"Liugaila","sequence":"additional","affiliation":[{"name":"6"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathleen","family":"Duffin","sequence":"additional","affiliation":[{"name":"1"},{"name":"4"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2324-672X","authenticated-orcid":true,"given":"Agnes","family":"Stefansdottir","sequence":"additional","affiliation":[{"name":"6"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4650-3765","authenticated-orcid":true,"given":"Rod T","family":"Mitchell","sequence":"additional","affiliation":[{"name":"1"},{"name":"4"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"416","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"2026070714144504300_bib1","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1530\/REP-14-0123","article-title":"Characterization of human spermatogonial stem cell markers in fetal, pediatric, and adult testicular tissues","volume":"148","author":"Altman","year":"2014","journal-title":"Reproduction"},{"key":"2026070714144504300_bib2","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/S2213-8587(15)00039-X","article-title":"Cancer treatment and gonadal function: experimental and established strategies for fertility preservation in children and young adults","volume":"3","author":"Anderson","year":"2015","journal-title":"Lancet Diabetes Endocrinol"},{"key":"2026070714144504300_bib3","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1200\/JCO.2008.21.1425","article-title":"Late mortality among 5-Year survivors of childhood cancer: a summary from the childhood cancer survivor study","volume":"27","author":"Armstrong","year":"2009","journal-title":"J Clin Oncol"},{"key":"2026070714144504300_bib4","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci Rep"},{"key":"2026070714144504300_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131573","article-title":"Multi-view semi-supervised feature selection with multi-order similarity and tensor learning","volume":"657","author":"Chen","year":"2025","journal-title":"Neurocomputing"},{"key":"2026070714144504300_bib6","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/S1470-2045(16)00086-3","article-title":"Pregnancy after chemotherapy in male and female survivors of childhood cancer treated between 1970 and 1999: a report from the childhood cancer survivor study cohort","volume":"17","author":"Chow","year":"2016","journal-title":"Lancet Oncol"},{"key":"2026070714144504300_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129761","article-title":"A dynamic tri-stage framework with neural network-assisted search for constrained multi-objective optimization","volume":"298","author":"Dang","year":"2026","journal-title":"Expert Syst Appl"},{"key":"2026070714144504300_bib8","first-page":"5.7.1","article-title":"Analysis of cellular DNA content by flow cytometry","volume":"119","author":"Darzynkiewicz","year":"2017","journal-title":"Curr Protoc Im"},{"key":"2026070714144504300_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131489","article-title":"Fault-tolerant synchronization of drive-response memristive competitive neural networks with multiple actuator failures","volume":"657","author":"Duan","year":"2025","journal-title":"Neurocomputing"},{"key":"2026070714144504300_bib10","doi-asserted-by":"crossref","first-page":"hoae010","DOI":"10.1093\/hropen\/hoae010","article-title":"A 20-year overview of fertility preservation in boys: new insights gained through a comprehensive international survey","volume":"2024","author":"Duffin","year":"2024","journal-title":"Hum Reprod Open"},{"key":"2026070714144504300_bib11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1152\/physrev.00013.2015","article-title":"Spermatogenesis: the commitment to meiosis","volume":"96","author":"Griswold","year":"2016","journal-title":"Physiol Rev"},{"key":"2026070714144504300_bib12","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.stem.2019.12.005","article-title":"The dynamic transcriptional cell Atlas of testis development during human puberty","volume":"26","author":"Guo","year":"2020","journal-title":"Cell Stem Cell"},{"key":"2026070714144504300_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129672","article-title":"Multi-objective portfolio optimization for stock return prediction using machine learning","volume":"298","author":"Huang","year":"2026","journal-title":"Expert Syst Appl"},{"key":"2026070714144504300_bib14","doi-asserted-by":"crossref","DOI":"10.1530\/RAF-24-0116","article-title":"Germ cell quantification in human fetal and prepubertal testis tissues: a comparison of current methodologies","volume":"6","author":"Kearney","year":"2025","journal-title":"Reprod Fertil"},{"key":"2026070714144504300_bib15","doi-asserted-by":"crossref","first-page":"28.6.1","DOI":"10.1002\/0471142727.mb2806s111","article-title":"Assaying cell cycle status using flow cytometry","volume":"111","author":"Kim","year":"2015","journal-title":"Curr Protoc Mol Biol"},{"key":"2026070714144504300_bib16","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.csbj.2024.12.033","article-title":"Machine learning methods for histopathological image analysis: updates in 2024","volume":"27","author":"Komura","year":"2025","journal-title":"Comput Struct Biotechnol J"},{"key":"2026070714144504300_bib17","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1111\/bjh.19572","article-title":"Decreased spermatogonial numbers in boys with severe haematological diseases","volume":"205","author":"Lahtinen","year":"2024","journal-title":"Br J Haematol"},{"key":"2026070714144504300_bib18","doi-asserted-by":"crossref","first-page":"3674","DOI":"10.3390\/ijms24043674","article-title":"Basic methods of cell cycle analysis","volume":"24","author":"Ligasov\u00e1","year":"2023","journal-title":"Int J Mol Sci"},{"key":"2026070714144504300_bib19","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1038\/ng.2653","article-title":"The genotype-tissue expression (GTEx) project","volume":"45","author":"Lonsdale","year":"2013","journal-title":"Nat Genet"},{"key":"2026070714144504300_bib20","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.toxrep.2020.12.023","article-title":"Chemotherapy induced damage to spermatogonial stem cells in prepubertal mouse in vitro impairs long-term spermatogenesis","volume":"8","author":"Lopes","year":"2021","journal-title":"Toxicol Rep"},{"key":"2026070714144504300_bib21","doi-asserted-by":"crossref","first-page":"5307","DOI":"10.1093\/bioinformatics\/btac677","article-title":"A novel pipeline for computerized mouse spermatogenesis staging","volume":"38","author":"Lu","year":"2022","journal-title":"Bioinformatics"},{"key":"2026070714144504300_bib22","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s43032-020-00357-6","article-title":"Histologic analysis of testes from prepubertal patients treated with chemotherapy associates impaired germ cell counts with cumulative doses of cyclophosphamide, ifosfamide, cytarabine, and asparaginase","volume":"28","author":"Medrano","year":"2021","journal-title":"Reprod Sci"},{"key":"2026070714144504300_bib23","doi-asserted-by":"crossref","first-page":"bqac202","DOI":"10.1210\/endocr\/bqac202","article-title":"STAGETOOL, a novel automated approach for mouse testis histological analysis","volume":"164","author":"Meikar","year":"2022","journal-title":"Endocrinology"},{"key":"2026070714144504300_bib24","doi-asserted-by":"crossref","first-page":"2755","DOI":"10.1093\/humrep\/den295","article-title":"Germ cell differentiation in the marmoset (Callithrix jacchus) during fetal and neonatal life closely parallels that in the human","volume":"23","author":"Mitchell","year":"2008","journal-title":"Hum Reprod"},{"key":"2026070714144504300_bib25","first-page":"1","article-title":"Deaths: final data for 2010","volume":"61","author":"Murphy","year":"2013","journal-title":"Natl Vital Stat Rep"},{"key":"2026070714144504300_bib26","doi-asserted-by":"crossref","first-page":"5223","DOI":"10.3390\/ijms20205223","article-title":"Oncofertility: pharmacological protection and immature testicular tissue (ITT)-based strategies for prepubertal and adolescent Male cancer patients","volume":"20","author":"Ntemou","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2026070714144504300_bib27","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/978-3-030-00934-2_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Schmidt","year":"2018","edition":"A Frangi, J Sch"},{"key":"2026070714144504300_bib28","doi-asserted-by":"crossref","first-page":"e75","DOI":"10.1016\/S1470-2045(17)30026-8","article-title":"Recommendations for gonadotoxicity surveillance in male childhood, adolescent, and young adult cancer survivors: a report from the International Late Effects of Childhood Cancer Guideline Harmonization Group in collaboration with the PanCareSurFup Consortium","volume":"18","author":"Skinner","year":"2017","journal-title":"Lancet Oncol"},{"key":"2026070714144504300_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129694","article-title":"EEG emotion recognition through a domain-adversarial multi-feature fusion network","volume":"298","author":"Sun","year":"2026","journal-title":"Expert Syst Appl"},{"key":"2026070714144504300_bib31","volume-title":"renv: project environments","author":"Ushey","year":"2025"},{"key":"2026070714144504300_bib32","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1093\/humrep\/dez043","article-title":"Testicular tissue cryopreservation: 8 years of experience from a coordinated network of academic centers","volume":"34","author":"Valli-Pulaski","year":"2019","journal-title":"Hum Reprod Oxf Engl"},{"key":"2026070714144504300_bib33","first-page":"83","article-title":"Childhood and adolescent cancer statistics, 2014","volume":"64","author":"Ward","year":"2014","journal-title":"CA Cancer J Clin"},{"key":"2026070714144504300_bib34","doi-asserted-by":"crossref","DOI":"10.1109\/WACV45572.2020.9093435","article-title":"Star-convex Polyhedra for 3D object detection and segmentation in microscopy","volume-title":"The IEEE Winter Conference on Applications of Computer Vision (WACV)","author":"Weigert","year":"2020"},{"key":"2026070714144504300_bib35","doi-asserted-by":"crossref","DOI":"10.1109\/ISBIC56247.2022.9854534","article-title":"Nuclei instance segmentation and classification in histopathology images with stardist","volume-title":"The IEEE International Symposium on Biomedical Imaging Challenges (ISBIC)","author":"Weigert","year":"2022"},{"key":"2026070714144504300_bib36","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome Atlas pan-cancer analysis project","volume":"45","author":"Weinstein","year":"2013","journal-title":"Nat Genet"},{"key":"2026070714144504300_bib37","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-24277-4","volume-title":"ggplot2: Elegant Graphics for Data Analysis","author":"Wickham","year":"2016"},{"key":"2026070714144504300_bib39","volume-title":"readr: Read Rectangular Text Data","author":"Wickham","year":"2024"},{"key":"2026070714144504300_bib38","volume-title":"dplyr: A Grammar of Data Manipulation","author":"Wickham","year":"2025"},{"key":"2026070714144504300_bib40","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101835","article-title":"Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis","volume":"70","author":"Xu","year":"2021","journal-title":"Med Image Anal"},{"key":"2026070714144504300_bib41","doi-asserted-by":"crossref","first-page":"5288","DOI":"10.1093\/bioinformatics\/btac673","article-title":"SATINN: an automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants","volume":"38","author":"Yang","year":"2022","journal-title":"Bioinformatics"}],"container-title":["Reproduction and Fertility"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/raf.bioscientifica.com\/view\/journals\/raf\/7\/2\/RAF-25-0210.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/raf.bioscientifica.com\/downloadpdf\/journals\/raf\/7\/2\/RAF-25-0210.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/journals.bioscientifica.com\/raf\/article-pdf\/doi\/10.1530\/RAF-25-0210\/521334\/raf-25-0210.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/journals.bioscientifica.com\/raf\/article-pdf\/doi\/10.1530\/RAF-25-0210\/521334\/raf-25-0210.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T18:14:54Z","timestamp":1783448094000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.bioscientifica.com\/raf\/article\/7\/2\/RAF250210\/77641\/Machine-learning-and-automation-methods-for-the"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,1]]},"references-count":40,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4,1]]}},"URL":"https:\/\/doi.org\/10.1530\/raf-25-0210","relation":{},"ISSN":["2633-8386"],"issn-type":[{"value":"2633-8386","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,1]]},"article-number":"RAF250210"}}