Micro-CT and machine learning: a high-throughput alternative to histology for follicle reserve assessment in cryopreserved ovarian tissue.

ABSTRACT:

Background

Ovarian tissue cryopreservation followed by transplantation after cancer remission is a fertility preservation strategy available for certain patient groups, such as pre-pubertal and adolescent girls, as well as adult females requiring urgent gonadotoxic therapy. Quantitative assessment of follicular density in cryopreserved cortical tissue is critical for evaluating tissue quality and estimating its reproductive potential. Conventional analysis, based on manual follicle counts in serial histological sections, is time-consuming, labor-intensive, and prone to variability from uneven follicle distribution and inconsistent tissue orientation. To address these limitations, we developed a high-throughput, automated method combining micro-CT, machine learning, and morphologica

SUBMITTER: Knuus K 

PROVIDER: S-EPMC12805687 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature