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

Evropi Toulkeridou, Carlos Enrique Gutierrez, Daniel Baum, Kenji Doya, Evan P. Economo
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引用次数: 0

摘要

显微ct成像已经成为形态生物学研究的便捷途径,以压倒性的速度产生数据。为了促进生态发现,微型ct生成的动物三维图像需要有效的处理和分析。然而,扫描标本内部部分的分割可能非常耗时。在这篇封面文章中。20230010年,Evropi Toulkeridou及其同事开发了一种基于深度学习的管道,用于精确、全自动地分割昆虫的微ct图像,该管道专为蚂蚁大脑设计,但可扩展到其他昆虫。此外,他们的注释数据集是第一个用于昆虫微ct图像的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Front Cover: Automated segmentation of insect anatomy from micro-CT images using deep learning

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.
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