Background Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces. Agronomists currently manually label photographs of roots obtained from rhizotrons using a line-intersect method to obtain root length density and rooting depth measurements which are essential for their experiments. We investigate the effectiveness of an automated image segmentation method based on the U-Net Convolutional Neural Network (CNN) architecture to enable such measurements. We design a data-set of 50 annotated chicory (Cichorium intybus L.) root images which we use to train, validate and test the system and compare against a baseline built using the Frangi vesselness filter. We obtain metrics using manual annotations and line-intersect counts. Results Our results on the held out data show our proposed automated segmentation system to be a viable solution for detecting and quantifying roots. We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748 and an r 2 of 0.9217. We also achieve an F 1 of 0.7 when comparing the automated segmentation to the manual annotations, with our automated segmentation system producing segmentations with higher quality than the manual annotations for large portions of the image. Conclusion We have demonstrated the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method. The success of our approach is also a demonstration of the feasibility of deep learning in practice for small research groups needing to create their own custom labelled dataset from scratch.

中文翻译：

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使用 U-Net 对土壤中的根进行分割。


背景植物根部研究可以提供一种获得耐胁迫作物的方法，这些作物在各种条件下都能获得更高的产量。由于难以接近根部并且使用耗时的手动方法，对土壤中的根进行表型分析通常具有挑战性。 Rhizotrons 允许通过透明表面目视检查根部生长。农学家目前使用线相交方法手动标记从根管获得的根的照片，以获得对其实验至关重要的根长密度和根深测量值。我们研究了基于 U-Net 卷积神经网络 (CNN) 架构的自动图像分割方法的有效性，以实现此类测量。我们设计了一个由 50 个带注释的菊苣 (Cichorium intybus L.) 根图像组成的数据集，用于训练、验证和测试系统，并与使用 Frangi 容器度过滤器构建的基线进行比较。我们使用手动注释和线相交计数来获取指标。结果我们对保留数据的结果表明，我们提出的自动分割系统是检测和量化根的可行解决方案。我们使用 867 个图像来评估我们的系统，我们已经获得了这些图像的线相交计数，获得了 0.9748 的 Spearman 等级相关性和 0.9217 的 r 2 。当将自动分割与手动注释进行比较时，我们还实现了 0.7 的 F 1 ，我们的自动分割系统对图像的大部分进行了比手动注释更高质量的分割。结论 我们已经证明了基于 U-Net 的 CNN 系统分割土壤中根部图像并取代手动线相交方法的可行性。 我们的方法的成功也证明了深度学习在实践中对于需要从头开始创建自己的自定义标记数据集的小型研究小组的可行性。