Kahveci, BurakOnen, SelinAkal, FuatKorkusuz, PetekBasic Sciences2024-07-052024-07-05202311058-04681573-733010.1007/s10815-023-02784-12-s2.0-85151377060https://doi.org/10.1007/s10815-023-02784-1https://hdl.handle.net/20.500.14411/2487Kahveci, Burak/0000-0001-7041-0986; Onen, Selin/0000-0002-3255-3035; KORKUSUZ, PETEK/0000-0002-7553-3915PurposeRapid and easy detection of spermatogonial stem/progenitor cells (SSPCs) is crucial for clinicians dealing with male infertility caused by prepubertal testicular damage. Deep learning (DL) methods may offer visual tools for tracking SSPCs on testicular strips of prepubertal animal models. The purpose of this study is to detect and count the seminiferous tubules and SSPCs in newborn mouse testis sections using a DL method.MethodsTesticular sections of the C57BL/6-type newborn mice were obtained and enumerated. Odd-numbered sections were stained with hematoxylin and eosin (H&E), and even-numbered sections were immune labeled (IL) with SSPC specific marker, SALL4. Seminiferous tubule and SSPC datasets were created using odd-numbered sections. SALL4-labeled sections were used as positive control. The YOLO object detection model based on DL was used to detect seminiferous tubules and stem cells.ResultsTest scores of the DL model in seminiferous tubules were obtained as 0.98 mAP, 0.93 precision, 0.96 recall, and 0.94 f1-score. The SSPC test scores were obtained as 0.88 mAP, 0.80 precision, 0.93 recall, and 0.82 f1-score.ConclusionSeminiferous tubules and SSPCs on prepubertal testicles were detected with a high sensitivity by preventing human-induced errors. Thus, the first step was taken for a system that automates the detection and counting process of these cells in the infertility clinic.eninfo:eu-repo/semantics/closedAccessSpermatogonial stemprogenitor cellsTestisYOLO object detectionDeep learningComputer visionArticleQ2Q240511871195WOS:00096085290000136995558