论文标题
在3D断层图中的生物样品本地化的自学学习
Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images
论文作者
论文摘要
在基于同步加速器的计算机断层扫描（CT）中，空间分辨率，视野和定位速度和样品对齐之间的权衡。对于高通量断层扫描的问题，这个问题甚至更为突出 - 一种自动设置，能够扫描大量没有人类相互作用的样本。结果，在许多应用中，只有20-30％的重建体积包含实际样本。此类数据冗余率缩小存储时间并增加处理时间。因此，自动样本定位成为一个重要的实际问题。在这项工作中，我们描述了为生物CT设计的两种自制损失。我们进一步演示了如何利用样本定位的不确定性估计。该方法显示了将样品定位小于1.5 \％相对误差的能力，并将使用的存储量减少四倍。我们还表明，提出的损失之一可以作为语义分割的预训练任务效果很好。
In synchrotron-based Computed Tomography (CT) there is a trade-off between spatial resolution, field of view and speed of positioning and alignment of samples. The problem is even more prominent for high-throughput tomography--an automated setup, capable of scanning large batches of samples without human interaction. As a result, in many applications, only 20-30% of the reconstructed volume contains the actual sample. Such data redundancy clutters the storage and increases processing time. Hence, an automated sample localization becomes an important practical problem. In this work, we describe two self-supervised losses designed for biological CT. We further demonstrate how to employ the uncertainty estimation for sample localization. This approach shows the ability to localize a sample with less than 1.5\% relative error and reduce the used storage by a factor of four. We also show that one of the proposed losses works reasonably well as a pre-training task for the semantic segmentation.
