High-throughput micro-CT scanning and deep learning segmentation workflow for analyses of shelly invertebrates and their fossils: Examples from marine Bivalvia
High-throughput micro-CT scanning and deep learning segmentation workflow for analyses of shelly invertebrates and their fossils: Examples from marine Bivalvia
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DOI：
10.3389/fevo.2023.1127756
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发表时间：
2023-03-08
影响因子：
3
通讯作者：
Jablonski, David
中科院分区：
文献类型：
--
作者：
Edie, Stewart M. M.;Collins, Katie S. S.;Jablonski, David
关于生命历史的经验数据的最大来源主要来自海洋无脊椎动物。它们丰富的化石记录是宏观生态学和宏观进化理论的重要试验场，但这种历史上的生物多样性大部分仍然被锁在固结的沉积物中。人工从基质中提取无脊椎动物化石可能需要数周到数月的仔细挖掘，并且不能保证恢复标本上的重要特征。Micro-CT极大地改善了我们对这些化石形态的了解，但仍然很难从相似成分的沉积物中分离出标本，例如，富含碳酸盐的基质中的钙质壳。在这里，我们提供了一个使用深度学习（基于人工神经网络的机器学习子集）的工作流程，以增强这些困难化石的分割。我们还提供了批量扫描化石和最新贝壳的指南，尺寸从1毫米到20厘米不等，能够快速获取大规模的3D数据集，用于宏观进化和宏观生态分析（8小时扫描300-500个贝壳）。然后，我们说明了这些方法已被用来访问新的层面的形态，允许严格的统计测试的空间和时间模式的形态进化，打开新的研究方向的历史上的生命。
The largest source of empirical data on the history of life largely derives from the marine invertebrates. Their rich fossil record is an important testing ground for macroecological and macroevolutionary theory, but much of this historical biodiversity remains locked away in consolidated sediments. Manually preparing invertebrate fossils out of their matrix can require weeks to months of careful excavation and cannot guarantee the recovery of important features on specimens. Micro-CT is greatly improving our access to the morphologies of these fossils, but it remains difficult to digitally separate specimens from sediments of similar compositions, e.g., calcareous shells in a carbonate rich matrix. Here we provide a workflow for using deep learning-a subset of machine learning based on artificial neural networks-to augment the segmentation of these difficult fossils. We also provide a guide for bulk scanning fossil and Recent shells, with sizes ranging from 1 mm to 20 cm, enabling the rapid acquisition of large-scale 3D datasets for macroevolutionary and macroecological analyses (300-500 shells in 8 hours of scanning). We then illustrate how these approaches have been used to access new dimensions of morphology, allowing rigorous statistical testing of spatial and temporal patterns in morphological evolution, which open novel research directions in the history of life.