Automated plankton classification from holographic imagery with deep convolutional neural networks
Automated plankton classification from holographic imagery with deep convolutional neural networks
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DOI：
10.1002/lom3.10402
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发表时间：
2020-12-03
影响因子：
2.7
通讯作者：
Hong, Jiarong
中科院分区：
文献类型：
--
作者：
Guo, Buyu;Nyman, Lisa;Hong, Jiarong
原位数字同轴全息术是一种可用于获取浮游生物高分辨率图像并以非侵入方式检查其在水柱内的时空分布的技术。然而，为了从数字全息图像中有效地鉴定生物体，有必要应用计算上昂贵的数值重建算法。这一漫长的过程阻碍了对浮游生物分布的实时监测。深度学习方法（例如卷积神经网络）应用于来自最低限度处理的全息图的不同生物体的干涉图案，可以消除重建的需要并实现实时计算。在本文中，我们将深度学习方法与数字内联全息技术相结合，为我们数据集中常见的10类生物创建一个快速准确的浮游生物分类网络。我们描述了从预处理到分类的过程。当应用于手动分类的测试数据集时，我们的网络达到了93.8%的准确率。在进一步应用概率过滤器消除错误分类后，平均准确率和召回率分别为96.8%和95.0%。此外，该网络被应用到7500原位全息图收集在东湾在华盛顿在垂直剖面，以表征当地硅藻的深度分布。结果与同时记录的独立叶绿素浓度深度分布一致。这个轻量级的网络证明了其实时，高精度浮游生物分类的能力，它有可能被部署在成像仪器上进行长期原位浮游生物监测。
In situ digital inline holography is a technique which can be used to acquire high-resolution imagery of plankton and examine their spatial and temporal distributions within the water column in a nonintrusive manner. However, for effective expert identification of an organism from digital holographic imagery, it is necessary to apply a computationally expensive numerical reconstruction algorithm. This lengthy process inhibits real-time monitoring of plankton distributions. Deep learning methods, such as convolutional neural networks, applied to interference patterns of different organisms from minimally processed holograms can eliminate the need for reconstruction and accomplish real-time computation. In this article, we integrate deep learning methods with digital inline holography to create a rapid and accurate plankton classification network for 10 classes of organisms that are commonly seen in our data sets. We describe the procedure from preprocessing to classification. Our network achieves 93.8% accuracy when applied to a manually classified testing data set. Upon further application of a probability filter to eliminate false classification, the average precision and recall are 96.8% and 95.0%, respectively. Furthermore, the network was applied to 7500 in situ holograms collected at East Sound in Washington during a vertical profile to characterize depth distribution of the local diatoms. The results are in agreement with simultaneously recorded independent chlorophyll concentration depth profiles. This lightweight network exemplifies its capability for real-time, high-accuracy plankton classification and it has the potential to be deployed on imaging instruments for long-term in situ plankton monitoring.