Digital diatom analysis: investigating advanced deep learning-based approaches for gigapixel-sized virtual slides

硅藻是一类特殊的微藻，在广泛的水生生境中发挥着重要的生态作用。在生态、古生态和应用研究中，对它们的硅酸盐壳层进行光学显微镜研究是确定它们组合组成的最古老但仍被广泛使用的方法之一。这些方法向数字对应方法的过渡已经进行了一段时间，有大量迹象表明，在生态/形态计量研究中，与一致性、透明度、精确度和统计能力有关的改进。到目前为止，这种数字硅藻分析大多只针对一个或几个分类群，或者在复杂群落的情况下需要大量的人工注释工作来进行分类鉴定。然而，初步研究现在已经明确，深度卷积网络可能很快就能使数字硅藻分析在物种丰富的群落中的“现实生活”条件下工作。我们确定了目前阻碍这一点的三个主要问题领域，涉及1)尽管背景复杂(由沉积物和其他颗粒引起)，但硅藻图像的分割；2)面对自然群落中凹形的等级-丰度关系，有效地增加训练数据集的分类覆盖率；3)大小、轮廓形状和纹理对于算法识别硅藻的相对重要性。我们希望通过一系列深度学习实验来解决这些问题，并为深度学习模型支持的数字硅藻分析在群落生态学、古生态学和其他类型的硅藻调查中的常规应用铺平道路。

Diatoms are a speciose group of microalgae playing important ecological roles in a broad range of aquatic habitats. Light microscopic investigation of their silicate shells is one of the oldest, but still widely used approaches to determining their assemblage composition in ecological, paleo-ecological and applied research. A transition of these methods to digital counterparts has been ongoing for some time now, with abundant indications for improvements related to consistency, transparency, precision and statistical power in ecological / morphometric studies. Thus far, such digital diatom analyses have mostly targeted only one or a few taxa, or required substantial manual annotation effort for taxonomic identification in the case of complex communities. Pilot studies have, however, now made it clear that deep convolutional networks will probably soon be able to also enable digital diatom analysis to work in “real life” conditions, with species rich communities. We identified three main problem fields presently hindering this, related to 1) segmentation of diatom images in spite of complex backgrounds (caused by sediment and other particles); 2) efficiently increasing taxonomic coverage of training data sets in the face of concave rank-abundance relationships in natural communities; and 3) the relative importance of size, outline shape and texture for algorithmic diatom identification. We would like to address these problems by a series of deep learning experiments and pave the way for a routine application of digital diatom analysis supported by deep learning models in community ecology, paleoecology and other types of diatom investigations.