Miika Vuorimaa, Ilona Kareinen, Petri Toivanen, Stefan Karlsson, Saku Ruohonen
{"title":"三甲基锡暴露后大鼠海马反应性星形胶质细胞的深度学习分割。","authors":"Miika Vuorimaa, Ilona Kareinen, Petri Toivanen, Stefan Karlsson, Saku Ruohonen","doi":"10.1177/01926233221124497","DOIUrl":null,"url":null,"abstract":"As regulators of homeostasis, astrocytes undergo morphological changes after injury to limit the insult in central nervous system (CNS). Trimethyltin (TMT) is a known neurotoxicant that induces reactive astrogliosis in rat CNS. To evaluate the degree of reactive astrogliosis, the assessment relies on manual counting or semiquantitative scoring. We hypothesized that deep learning algorithm could be used to identify the grade of reactive astrogliosis in immunoperoxidase-stained sections in a quantitative manner. The astrocyte algorithm was created using a commercial supervised deep learning platform and the used training set consisted of 940 astrocytes manually annotated from hippocampus and cortex. Glial fibrillary acidic protein-labeled brain sections of rat TMT model were analyzed for astrocytes with the trained algorithm. Algorithm was able to count the number of individual cells, cell areas, and circumferences. The astrocyte algorithm identified astrocytes with varying sizes from immunostained sections with high confidence. Algorithm analysis data revealed a novel morphometric marker based on cell area and circumference. This marker correlated with the time-dependent progression of the neurotoxic profile of TMT. This study highlights the potential of using novel deep learning-based image analysis tools in neurotoxicity and pharmacology studies.","PeriodicalId":23113,"journal":{"name":"Toxicologic Pathology","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Deep Learning-Based Segmentation of Morphologically Distinct Rat Hippocampal Reactive Astrocytes After Trimethyltin Exposure.\",\"authors\":\"Miika Vuorimaa, Ilona Kareinen, Petri Toivanen, Stefan Karlsson, Saku Ruohonen\",\"doi\":\"10.1177/01926233221124497\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As regulators of homeostasis, astrocytes undergo morphological changes after injury to limit the insult in central nervous system (CNS). Trimethyltin (TMT) is a known neurotoxicant that induces reactive astrogliosis in rat CNS. To evaluate the degree of reactive astrogliosis, the assessment relies on manual counting or semiquantitative scoring. We hypothesized that deep learning algorithm could be used to identify the grade of reactive astrogliosis in immunoperoxidase-stained sections in a quantitative manner. The astrocyte algorithm was created using a commercial supervised deep learning platform and the used training set consisted of 940 astrocytes manually annotated from hippocampus and cortex. Glial fibrillary acidic protein-labeled brain sections of rat TMT model were analyzed for astrocytes with the trained algorithm. Algorithm was able to count the number of individual cells, cell areas, and circumferences. The astrocyte algorithm identified astrocytes with varying sizes from immunostained sections with high confidence. Algorithm analysis data revealed a novel morphometric marker based on cell area and circumference. This marker correlated with the time-dependent progression of the neurotoxic profile of TMT. This study highlights the potential of using novel deep learning-based image analysis tools in neurotoxicity and pharmacology studies.\",\"PeriodicalId\":23113,\"journal\":{\"name\":\"Toxicologic Pathology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Toxicologic Pathology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/01926233221124497\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/9/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"PATHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Toxicologic Pathology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/01926233221124497","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/9/20 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"PATHOLOGY","Score":null,"Total":0}
Deep Learning-Based Segmentation of Morphologically Distinct Rat Hippocampal Reactive Astrocytes After Trimethyltin Exposure.
As regulators of homeostasis, astrocytes undergo morphological changes after injury to limit the insult in central nervous system (CNS). Trimethyltin (TMT) is a known neurotoxicant that induces reactive astrogliosis in rat CNS. To evaluate the degree of reactive astrogliosis, the assessment relies on manual counting or semiquantitative scoring. We hypothesized that deep learning algorithm could be used to identify the grade of reactive astrogliosis in immunoperoxidase-stained sections in a quantitative manner. The astrocyte algorithm was created using a commercial supervised deep learning platform and the used training set consisted of 940 astrocytes manually annotated from hippocampus and cortex. Glial fibrillary acidic protein-labeled brain sections of rat TMT model were analyzed for astrocytes with the trained algorithm. Algorithm was able to count the number of individual cells, cell areas, and circumferences. The astrocyte algorithm identified astrocytes with varying sizes from immunostained sections with high confidence. Algorithm analysis data revealed a novel morphometric marker based on cell area and circumference. This marker correlated with the time-dependent progression of the neurotoxic profile of TMT. This study highlights the potential of using novel deep learning-based image analysis tools in neurotoxicity and pharmacology studies.
期刊介绍:
Toxicologic Pathology is dedicated to the promotion of human, animal, and environmental health through the dissemination of knowledge, techniques, and guidelines to enhance the understanding and practice of toxicologic pathology. Toxicologic Pathology, the official journal of the Society of Toxicologic Pathology, will publish Original Research Articles, Symposium Articles, Review Articles, Meeting Reports, New Techniques, and Position Papers that are relevant to toxicologic pathology.