SATINN v2:多实验室数据集成的小鼠睾丸组织学自动图像分析。

IF 3.1 2区 生物学 Q2 REPRODUCTIVE BIOLOGY
Ran Yang, Fritzie T Celino-Brady, Jessica E M Dunleavy, Katinka A Vigh-Conrad, Georgia R Atkins, Rachel L Hvasta, Christopher R X Pombar, Alexander N Yatsenko, Kyle E Orwig, Moira K O'Bryan, Ana C Lima, Donald F Conrad
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引用次数: 0

摘要

睾丸组织学分析是研究男性生育能力的基础,但这是一项缓慢的任务,需要很高的技能门槛。在这里,我们描述了新的神经网络模型,用于自动分类细胞类型和小管阶段从小鼠睾丸的全幻灯片亮场图像。细胞类型分类器识别14种细胞类型,包括减数分裂I前期的多个步骤,外部验证准确率为96%。管级分类器区分所有12个典型的管级,外部验证精度为63%,当允许±1级公差时,该精度增加到96%。我们通过广泛的训练多样化和对外部(非训练种群)野生型和突变型数据集的测试来解决SATINN的泛化性。这使我们能够使用SATINN成功地处理多个实验室生成的数据。我们使用SATINN分析了来自3个不同实验室的8个不同突变系的睾丸图像,这些突变系采用一系列组织处理方案。最后,我们表明可以使用SATINN输出在潜在空间中聚类组织学图像,当应用于8个突变系时,揭示了它们病理中的已知关系。这项工作代表了一个强大的、自动化的睾丸组织病理学工具的重大进展,可以被多个实验室使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SATINN v2: automated image analysis for mouse testis histology with multi-laboratory data integration†.

Analysis of testis histology is fundamental to the study of male fertility, but it is a slow task with a high skill threshold. Here, we describe new neural network models for the automated classification of cell types and tubule stages from whole-slide brightfield images of mouse testis. The cell type classifier recognizes 14 cell types, including multiple steps of meiosis I prophase, with an external validation accuracy of 96%. The tubule stage classifier distinguishes all 12 canonical tubule stages with external validation accuracy of 63%, which increases to 96% when allowing for ±1 stage tolerance. We addressed generalizability of SATINN, through extensive training diversification and testing on external (non-training population) wildtype and mutant datasets. This allowed us to use SATINN to successfully process data generated in multiple laboratories. We used SATINN to analyze testis images from eight different mutant lines, generated from three different labs with a range of tissue processing protocols. Finally, we show that it is possible to use SATINN output to cluster histology images in latent space, which, when applied to the eight mutant lines, reveals known relationships in their pathology. This work represents significant progress towards a tool for robust, automated testis histopathology that can be used by multiple labs.

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来源期刊
Biology of Reproduction
Biology of Reproduction 生物-生殖生物学
CiteScore
6.30
自引率
5.60%
发文量
214
审稿时长
1 months
期刊介绍: Biology of Reproduction (BOR) is the official journal of the Society for the Study of Reproduction and publishes original research on a broad range of topics in the field of reproductive biology, as well as reviews on topics of current importance or controversy. BOR is consistently one of the most highly cited journals publishing original research in the field of reproductive biology.
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