Evropi Toulkeridou, Carlos Enrique Gutierrez, Daniel Baum, Kenji Doya, Evan P. Economo
{"title":"封面:利用深度学习从微型ct图像中自动分割昆虫解剖结构","authors":"Evropi Toulkeridou, Carlos Enrique Gutierrez, Daniel Baum, Kenji Doya, Evan P. Economo","doi":"10.1002/ntls.202340001","DOIUrl":null,"url":null,"abstract":"Micro-CT imaging has become readily accessible for morphological biology studies, generating data at overwhelming rates. To facilitate ecological discovery, micro-CT-produced 3D images of animals need efficient processing and analysis. However, segmentation of inner parts of scanned specimens can be very time-consuming. In this cover article ntls.20230010, Evropi Toulkeridou and colleagues develop a deep learning-based pipeline for accurate, fully automated segmentation of micro-CT images of insects, designed for ant brains but extendable to other insects. Further, their annotated dataset is among the first for micro-CT images of insects.","PeriodicalId":501225,"journal":{"name":"Natural Sciences","volume":"43 49","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Front Cover: Automated segmentation of insect anatomy from micro-CT images using deep learning\",\"authors\":\"Evropi Toulkeridou, Carlos Enrique Gutierrez, Daniel Baum, Kenji Doya, Evan P. Economo\",\"doi\":\"10.1002/ntls.202340001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Micro-CT imaging has become readily accessible for morphological biology studies, generating data at overwhelming rates. To facilitate ecological discovery, micro-CT-produced 3D images of animals need efficient processing and analysis. However, segmentation of inner parts of scanned specimens can be very time-consuming. In this cover article ntls.20230010, Evropi Toulkeridou and colleagues develop a deep learning-based pipeline for accurate, fully automated segmentation of micro-CT images of insects, designed for ant brains but extendable to other insects. Further, their annotated dataset is among the first for micro-CT images of insects.\",\"PeriodicalId\":501225,\"journal\":{\"name\":\"Natural Sciences\",\"volume\":\"43 49\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Natural Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/ntls.202340001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/ntls.202340001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Front Cover: Automated segmentation of insect anatomy from micro-CT images using deep learning
Micro-CT imaging has become readily accessible for morphological biology studies, generating data at overwhelming rates. To facilitate ecological discovery, micro-CT-produced 3D images of animals need efficient processing and analysis. However, segmentation of inner parts of scanned specimens can be very time-consuming. In this cover article ntls.20230010, Evropi Toulkeridou and colleagues develop a deep learning-based pipeline for accurate, fully automated segmentation of micro-CT images of insects, designed for ant brains but extendable to other insects. Further, their annotated dataset is among the first for micro-CT images of insects.