Root Gap Correction with a Deep Inpainting Model
Root Gap Correction with a Deep Inpainting Model
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发表时间：
2018-09
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通讯作者：
Hao Chen;M. Giuffrida;S. Tsaftaris;P. Doerner
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作者：
Hao Chen;M. Giuffrida;S. Tsaftaris;P. Doerner
以非侵入性且经济实惠的方式对正在生长的植物的根部进行成像一直是图像辅助植物育种和表型分析中长期存在的问题。最经济和最广泛的方法之一是使用中生态系统，其中植物生长在玻璃表面的土壤中，允许根部可视化和成像。然而，由于土壤以及植物根部是 3D 对象的 2D 投影这一事实，部分根部被遮挡。因此，即使在完美的根分割下，生成的图像也包含一些间隙，可能会阻碍细粒度根系统结构特征的提取。我们提出了一种有效的深度神经网络来恢复断开的根段的间隙。我们训练一个完全监督的编码器-解码器深度 CNN，给定包含间隙的图像作为输入，生成修复版本，恢复丢失的部分。由于在真实数据中缺乏真实数据，我们使用合成根图像 [10]，通过引入间隙来人为扰乱这些图像来训练和评估我们的方法。我们表明，我们的网络可以在双子叶植物和单子叶植物情况下发挥作用，减少根间隙。我们还在鹰嘴豆根结构的实际数据中展示了有希望的示范结果。
Imaging roots of growing plants in a non-invasive and affordable fashion has been a long-standing problem in image-assisted plant breeding and phenotyping. One of the most affordable and diffuse approaches is the use of mesocosms, where plants are grown in soil against a glass surface that permits the roots visualization and imaging. However, due to soil and the fact that the plant root is a 2D projection of a 3D object, parts of the root are occluded. As a result, even under perfect root segmentation, the resulting images contain several gaps that may hinder the extraction of finely grained root system architecture traits. We propose an effective deep neural network to recover gaps from disconnected root segments. We train a fully supervised encoder-decoder deep CNN that, given an image containing gaps as input, generates an inpainted version, recovering the missing parts. Since in real data ground-truth is lacking, we use synthetic root images [10] that we artificially perturb by introducing gaps to train and evaluate our approach. We show that our network can work both in dicot and monocot cases in reducing root gaps. We also show promising exemplary results in real data from chickpea root architectures.