First-order machine learning based detection and classification of foraminifera in marine sediments from Arctic environments
First-order machine learning based detection and classification of foraminifera in marine sediments from Arctic environments
复制标题
基于一阶机器学习的北极环境海洋沉积物中有孔虫的检测和分类
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
J. Junttila
中科院分区：
文献类型：
--
作者：
S. Aagaard;Thomas Haugland Johansen;J. Junttila
有孔虫是微小的单细胞生物，在海洋领域无处不在，在它们的生命周期中形成壳。一般来说，这些贝壳在沉积物中可以很好地石化，并且由于物种间的形态和纹饰变化，它们是可诊断的。有孔虫壳的分类和计数是评估和重建过去和现在的环境、海洋和气候条件的重要工具。然而，目前的人工鉴定程序，用显微镜和针/刷进行，是非常耗时的。使用机器学习绕过这个手动过程，有望大大降低与生成有孔虫数据记录相关的时间消耗。实现这一目标的第一步是开发一种深度学习模型，该模型可以从2D数字显微镜图片中检测和分类微观有孔虫。这项工作是基于在ImageNet数据集上预训练的VGG16模型实现，并采用迁移学习技术使模型适应有孔虫任务。2D摄影训练数据输入是通过结合来自巴伦支海地区的北极海洋沉积物（100µ；m-1mm大小的分数）的代表性和提取的对象来构建的。训练数据构建采用4个对象组，分别为1)钙质和2)凝集底栖有孔虫、3)浮游有孔虫和4)沉积物。通过最初的设置，算法能够正确识别四组中的一组，正确率约为90%，并且经过进一步的微调和改进，正确率达到98%。第二步是使用机器学习对沉积物中单个底栖钙质有孔虫进行分类。这项工作将集中在20种最常见的物种上，它们占北极底栖钙质有孔虫动物群总数的80%。除了可能使用高光谱成像之外，算法的训练将使用目标物种特定的2D摄影和3D CT扫描数据
<p>Foraminifera are microscopic single-celled organisms, ubiquitous to the marine realm, that construct shells during their life cycle. The shells, in general, fossilize well in the sediment and they are diagnosable due to inter-species morphology and ornamentation variability. Classifying and counting foraminiferal shells is an important tool in assessing and reconstructing past and present environmental, oceanographic and climatological conditions. However, the present day manual identification procedure, performed with a microscope and a needle/brush, is a very time consuming. Circumventing this manual procedure, using machine leaning, promises to dramatically lower the time consumption related to generating foraminiferal data records.</p><p>The first step towards that end is developing a deep learning model that can detect and classify microscopic foraminifera from 2D digital microscope pictures. The work is based on a VGG16 model implementation that has been pre trained on the ImageNet dataset and employing transfer learning techniques to adapt the model to the foraminifera task. The 2D photographic training data input was constructed by combining objects representative of and extracted from Arctic marine sediments (100µm-1mm size fraction) from the Barents Sea region. Four object groups, including 1) calcareous and 2) agglutinated benthic foraminifera, 3) planktic foraminifera and 4) sediments were used in the training data construction. With the initial set-up the algorithms were able to identify adherence to one of the four groups correctly ~90% of the time and with further fine-tuning and refinement reaching 98% correct identifications.</p><p>The second step is to use machine leaning for classification of individual benthic calcareous foraminiferal species within the sediment. The work will focus on the 20 most common species that comprise ca. ≥ 80% of the total benthic calcareous foraminiferal fauna in the Arctic. The training of the algorithms will be done using targeted species-specific 2D photographic and 3D CT scanning data in addition to potentially using hyperspectral imaging.</p>