论文标题
3D U-NET，用于分割超分辨率的植物根部MRI图像
3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution
论文作者
论文摘要
磁共振成像（MRI）使植物科学家可以非侵入研究根系的发展和根部土壤相互作用。不过，挑战性记录条件，例如低分辨率和高水平的噪声阻碍了传统根源提取算法的性能。我们建议通过使用3D U-NET将扫描的体积分割成超分辨率的根和土壤，从而提高信噪比和分辨率。实际数据上的测试表明，训练有素的网络能够成功地检测大多数根源，甚至发现人类注释遗失的根源。我们的实验表明，通过修改损失函数，可以进一步提高分割性能。
Magnetic resonance imaging (MRI) enables plant scientists to non-invasively study root system development and root-soil interaction. Challenging recording conditions, such as low resolution and a high level of noise hamper the performance of traditional root extraction algorithms, though. We propose to increase signal-to-noise ratio and resolution by segmenting the scanned volumes into root and soil in super-resolution using a 3D U-Net. Tests on real data show that the trained network is capable to detect most roots successfully and even finds roots that were missed by human annotators. Our experiments show that the segmentation performance can be further improved with modifications of the loss function.
