Quantification of follicles in human ovarian tissue using image processing software and trained artificial intelligence

生物 毛囊 卵泡 卵巢癌 内分泌系统 人工智能 卵巢 癌症 计算机科学 内分泌学 激素 遗传学
作者
Gabrielle M. Blevins,Colleen L. Flanagan,Sridula S Kallakuri,Owen M Meyer,Likitha Nimmagadda,James D Hatch,Sydney A Shea,Vasantha Padmanabhan,Ariella Shikanov
出处
期刊:Biology of Reproduction [Oxford University Press]
卷期号:110 (6): 1086-1099 被引量:2
标识
DOI:10.1093/biolre/ioae048
摘要

Abstract Cancer survival rates in prepubertal girls and young women have risen in recent decades due to increasingly efficient treatments. However, many such treatments are gonadotoxic, causing premature ovarian insufficiency, loss of fertility, and ovarian endocrine function. Implantation of donor ovarian tissue encapsulated in immune-isolating capsules is a promising method to restore physiological endocrine function without immunosuppression or risk of reintroducing cancer cells harbored by the tissue. The success of this approach is largely determined by follicle density in the implanted ovarian tissue, which is analyzed manually from histologic sections and necessitates specialized, time-consuming labor. To address this limitation, we developed a fully automated method to quantify follicle density that does not require additional coding. We first analyzed ovarian tissue from 12 human donors between 16 and 37 years old using semi-automated image processing with manual follicle annotation and then trained artificial intelligence program based on follicle identification and object classification. One operator manually analyzed 102 whole slide images from serial histologic sections. Of those, 77 images were assessed by a second manual operator, followed with an automated method utilizing artificial intelligence. Of the 1181 follicles the control operator counted, the comparison operator counted 1178, and the artificial intelligence counted 927 follicles with 80% of those being correctly identified as follicles. The three-stage artificial intelligence pipeline finished 33% faster than manual annotation. Collectively, this report supports the use of artificial intelligence and automation to select tissue donors and grafts with the greatest follicle density to ensure graft longevity for premature ovarian insufficiency treatment.
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