Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Turku, Finland.
Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Turku, Finland.
Am J Pathol. 2023 Aug;193(8):1072-1080. doi: 10.1016/j.ajpath.2023.04.014. Epub 2023 May 24.
The incidence of nonalcoholic fatty liver disease is a continuously growing health problem worldwide, along with obesity. Therefore, novel methods to both efficiently study the manifestation of nonalcoholic fatty liver disease and to analyze drug efficacy in preclinical models are needed. The present study developed a deep neural network-based model to quantify microvesicular and macrovesicular steatosis in the liver on hematoxylin-eosin-stained whole slide images, using the cloud-based platform, Aiforia Create. The training data included a total of 101 whole slide images from dietary interventions of wild-type mice and from two genetically modified mouse models with steatosis. The algorithm was trained for the following: to detect liver parenchyma, to exclude the blood vessels and any artefacts generated during tissue processing and image acquisition, to recognize and differentiate the areas of microvesicular and macrovesicular steatosis, and to quantify the recognized tissue area. The results of the image analysis replicated well the evaluation by expert pathologists and correlated well with the liver fat content measured by EchoMRI ex vivo, and the correlation with total liver triglycerides was notable. In conclusion, the developed deep learning-based model is a novel tool for studying liver steatosis in mouse models on paraffin sections and, thus, can facilitate reliable quantification of the amount of steatosis in large preclinical study cohorts.
非酒精性脂肪性肝病的发病率是一个全球性的、不断增长的健康问题，伴随着肥胖症。因此，需要新的方法来有效地研究非酒精性脂肪性肝病的表现，并在临床前模型中分析药物疗效。本研究开发了一种基于深度神经网络的模型，用于在苏木精-伊红染色的全切片图像上定量肝内微小囊泡和大囊泡脂肪变性，使用基于云的平台 Aiforia Create。训练数据包括来自野生型小鼠饮食干预和两种具有脂肪变性的基因修饰小鼠模型的总共 101 张全切片图像。该算法经过训练可以执行以下操作：检测肝实质，排除血管和组织处理及图像采集过程中产生的任何伪影，识别和区分微小囊泡和大囊泡脂肪变性区域，并对识别出的组织区域进行量化。图像分析的结果与专家病理学家的评估吻合良好，与体外 EchoMRI 测量的肝脂肪含量以及与总肝甘油三酯的相关性也很好。总之，开发的基于深度学习的模型是一种研究石蜡切片中小鼠模型肝脂肪变性的新工具，因此可以促进在大型临床前研究队列中对脂肪变性量的可靠定量。
