Nathan Siu, Maxime Ruiz, Sheyla González Garrido, Yu Yan, Dylan Steinecke, Elizabeth Rao, Rachel Choi, S. Robertson, S. Deng, C. Arnold, W. Speier
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引用次数: 0
摘要
角膜缘干细胞缺乏症(LSCD)是一种进行性角膜疾病,导致角膜上皮无法自我修复,最终导致视力丧失。技术的进步使得角膜缘干细胞的体外生长能够用于移植。用于评估这些培养细胞质量的一种方法是细胞密度,这通常是由专家手动计算的,这是耗时的,并且具有很高的比率变异性。这个项目的目标是创建一个工具,可以从培养细胞的数字图像中自动计算细胞密度。结果与四位不同经验水平的专家的注释进行了比较。细胞计数与专家注释高度相关(r=0.64, p<0.01)。与临床经验较低的人类注释者相比,该算法与经验丰富的注释者的一致性明显更好(r=0.75 vs r=0.19, p<0.01)。这些结果表明,自动化工具可以提供有意义的细胞密度计数,这可以潜在地提高注释一致性并减少评估LSCD细胞培养所需的时间。
Automatic Estimation of Limbal Stem Cell Densities in Cultured Epithelial Cell Microscopy Imaging
Limbal stem cell deficiency (LSCD) is a progressive corneal disease that renders the corneal epithelium unable to repair itself, which can lead to the eventual loss of vision. Advances in technology have allowed for the growth of limbal stem cells ex-vivo for the purposes of transplantation. One method used to evaluate the quality of these cultivated cells is cell density, which is typically calculated manually by experts, which is time-consuming and has high inter-rater variability. The goal of this project was to create a tool that automatically calculates cell density from digital images of the cultured cells. Results were compared against annotations from four experts with varying levels of experience. Cell counts had high correlation with expert annotations (r=0.64, p<0.01). When compared to human annotators with lower clinical experience, the algorithm achieved significantly better agreement with highly experienced annotators (r=0.75 vs r=0.19, p<0.01). These results suggest that the automated tool can provide meaningful cell density counts, which can potentially improve annotation consistency and reduce time required for evaluating LSCD cell cultures.