论文标题
MRI的植物根结构重建的稳健骨架化
Robust Skeletonization for Plant Root Structure Reconstruction from MRI
论文作者
论文摘要
由于低分辨率和3D测量值的信噪比低，可能导致断开连接和错误连接的根，因此从MRI中植物根的结构重建是具有挑战性的。我们为此任务提出了两阶段的方法。第一阶段是基于语义根和土壤分割的，并且发现从任何根体素到芽的最低成本路径。第二阶段采用了在第一阶段生成的最大完全连接的组件，并使用3D骨架来提取图形结构。我们在22次MRI扫描中评估了我们的方法，并与人类专家的重建进行了比较。
Structural reconstruction of plant roots from MRI is challenging, because of low resolution and low signal-to-noise ratio of the 3D measurements which may lead to disconnectivities and wrongly connected roots. We propose a two-stage approach for this task. The first stage is based on semantic root vs. soil segmentation and finds lowest-cost paths from any root voxel to the shoot. The second stage takes the largest fully connected component generated in the first stage and uses 3D skeletonization to extract a graph structure. We evaluate our method on 22 MRI scans and compare to human expert reconstructions.
