背景：在基础植物研究和有选择地培育有韧性的作物显示了对非侵入性 3D 成像和连续 3D 分割植物根系的兴趣。目前最先进的技术包括计算机断层扫描 （CT） 扫描和重建，然后进行充分的3D分割过程。

挑战：由于土壤成分不均匀以及根结构变异规模大，生成根部的精确3D分割具有挑战性。

方法：（1）我们通过结合深度卷积神经来应对挑战具有弱监督学习范式的网络 （DCNN）；此外，（2）我们应用空间金字塔池化 （SPP） 层来研究根；（3）我们使用专门的子标记技术生成一个微调的训练数据集；（4）最后，为了产生快速和高质量的细分，我们提出一种专门的迭代推理算法，该算法对字段进行局部适配网络的视图 （FoV）。

实验：我们将分割结果与根分割分析参考算法参考算法 （RootForce）分割的一组木薯根比较，定性地表明可分割的根体素和根分支数量增加。

结果：我们的研究结果表明，将所提出的 DCNN 方法相结合通过动态推理，可做更多根结构，与使用经典的分析参考方法相比，更易于检测根精细结构。

结论：研究表明，所提出的DCNN方法的应用可实现更好的根分割，特别是对于非常小和细的根。

3D segmentation of plant root systems using spatial pyramid pooling and locally adaptive field-of-view inference

Background: The non-invasive 3D-imaging and successive 3D-segmentation of plant root systems has gained interest within fundamental plant research and selectively breeding resilient crops. Currently the state of the art consists of computed tomography (CT) scans and reconstruction followed by an adequate 3D-segmentation process. Challenge: Generating an exact 3D -segmentation of the roots becomes challenging due to inhomogeneous soil composition, as well as high scale variance in the root structures themselves. Approach: (1) We address the challenge by combining deep convolutional neural networks (DCNNs) with a weakly supervised learning paradigm. Furthermore, (2) we apply a spatial pyramid pooling (SPP) layer to cope with the scale variance of roots. (3) We generate a fifine-tuned training data set with a specialized sublabeling technique. (4) Finally, to yield fast and high-quality segmentations, we propose a specialized iterative inference algorithm, which locally adapts the fifield of view (FoV) for the network. 

Experiments: We compare our segmentation results against an analytical reference algorithm for root segmentation (RootForce) on a set of roots from Cassava plants and show qualitatively that an increased amount of root voxels and root branches can be segmented. 

Results: Our fifindings show that with the proposed DCNN approach combined with the dynamic inference, much more, and especially fifine, root structures can be detected than with a classical analytical reference method.

Conclusion: We show that the application of the proposed DCNN approach leads to better and more robust root segmentation, especially for very small and thin roots.