AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-10-01 Epub Date: 2024-08-07 DOI:10.1007/s12021-024-09679-1
Roman Peter, Petr Hrobar, Josef Navratil, Martin Vagenknecht, Jindrich Soukup, Keiko Tsuji, Nestor X Barrezueta, Anna C Stoll, Renee C Gentzel, Jonathan A Sugam, Jacob Marcus, Danny A Bitton
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Abstract

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

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AnNoBrainer,利用深度学习自动标注小鼠大脑图像。
对整个小鼠大脑的多个感兴趣区域进行标注是对神经科学数字病理学中的多种研究终点进行定量评估的一个不可或缺的过程。事先经验和领域专家知识是保证图像标注质量和一致性的关键因素。目前,图像注释通常由经过认证的病理学家或训练有素的技术人员手工完成,这限制了神经科学数字病理实验室的研究总吞吐量。这也可能意味着非病理学家会使用更简单快捷的方法来检查组织样本,尤其是在研究和临床前研究的早期阶段。为了解决这些局限性并满足制药领域对图像分析日益增长的需求,我们开发了 AnNoBrainer,这是一款开源软件工具,它利用深度学习、图像注册和标准皮层脑模板自动注释二维病理切片上的单个脑区。将 AnNoBrainer 应用于一组已发表的突触核蛋白病转基因小鼠模型病理切片后发现,与经过培训的病理学科学家相比,AnNoBrainer 的准确性相当高,可重复性也有所提高,而且在大脑注释、质量控制和标记方面所花费的时间显著减少(约 50%)。总之,AnNoBrainer 提供了一种快速、准确、可重复的小鼠大脑图像自动标注方法,在很大程度上达到了专家的组织病理学评估标准(> 85% 的病例),并实现了数字病理实验室的高通量图像分析工作流程。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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