PDF(Pubmed)
Abstract：
BACKGROUND: X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes.
RESULTS: VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data.
CONCLUSIONS: A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.
RESULTS: VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data.
CONCLUSIONS: A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.
摘要：
背景：X射线计算机断层扫描（CT）是测量土壤中植物根系生长的有力工具。然而,用更大的罐子快速扫描，这是吞吐量优先的作物育种所必需的，导致高噪声水平，低分辨率,CT体积中的根段模糊。此外，虽然植物根系分割对于根系量化至关重要，关于分割嘈杂根段的详细条件研究很少。本研究旨在研究扫描时间和基于深度学习的图像质量恢复对CT体积中模糊水稻（Oryzasativa）根段语义分割的影响。
结果：VoxResNet，基于卷积神经网络的逐体素残差网络，被用作分割模型。使用在33、66、150、300和600s的扫描时间获得的CT体积比较模型的训练效率。样本的学习效率相似，除了33和66s的扫描时间。此外，预测体积的噪声水平因扫描条件而异，说明扫描时间≥150s的噪声水平不影响模型训练效率。传统的过滤方法，如中值滤波和边缘检测，在任何条件下，培训效率都提高了约10%。然而,33和66s扫描样本的训练效率仍然相对较低。我们得出结论，扫描时间必须至少为150s，以免影响分割。最后,我们构建了150个s扫描CT体积的语义分割模型，骰子损失达到0.093。该模型无法预测侧根，这些数据不包括在训练数据中。这种限制将通过准备适当的训练数据来解决。
结论：即使使用具有高噪声水平的快速扫描CT体积，也可以构建语义分割模型。鉴于扫描时间≥150s不影响分割结果，这种技术有望用于快速和低剂量扫描。这项研究提供了对具有高噪声水平的CT体积以外的图像的见解，这些图像在注释时具有挑战性。
结果：VoxResNet，基于卷积神经网络的逐体素残差网络，被用作分割模型。使用在33、66、150、300和600s的扫描时间获得的CT体积比较模型的训练效率。样本的学习效率相似，除了33和66s的扫描时间。此外，预测体积的噪声水平因扫描条件而异，说明扫描时间≥150s的噪声水平不影响模型训练效率。传统的过滤方法，如中值滤波和边缘检测，在任何条件下，培训效率都提高了约10%。然而,33和66s扫描样本的训练效率仍然相对较低。我们得出结论，扫描时间必须至少为150s，以免影响分割。最后,我们构建了150个s扫描CT体积的语义分割模型，骰子损失达到0.093。该模型无法预测侧根，这些数据不包括在训练数据中。这种限制将通过准备适当的训练数据来解决。
结论：即使使用具有高噪声水平的快速扫描CT体积，也可以构建语义分割模型。鉴于扫描时间≥150s不影响分割结果，这种技术有望用于快速和低剂量扫描。这项研究提供了对具有高噪声水平的CT体积以外的图像的见解，这些图像在注释时具有挑战性。
