Abstract
X-ray computed tomography (CT) is a widely used imaging technique that provides detailed examinations into the internal structure of an object with synchrotron CT (SR-CT) enabling improved data quality by using higher energy, monochromatic X-rays. While SR-CT allows for improved resolution, time-resolved experimentation, and reduced imaging artifacts, it also produces significantly larger datasets than conventional CT. Accurate and efficient evaluation of these datasets is a critical component of these workflows; yet is often done manually representing a major bottleneck in the analysis phase. While deep learning has emerged as a powerful tool capable of providing a wide range of purely data-driven solutions, it requires a substantial amount of labeled data for training and manual annotation of SR-CT datasets is impractical in practice. In this paper, we introduce a novel framework that enables automatic segmentation of large, high-resolution SR-CT datasets by eliminating the need to hand label images for deep learning training. First, we generate pseudo labels by clustering on the voxel values identifying regions in the volume with similar attenuation coefficients producing an initial semantic map. Afterwards, we train a segmentation model on the pseudo labels before utilizing the Unbiased Teacher approach to self-correct them ensuring accurate final segmentations. We find our approach improves pixel-wise accuracy and mIoU by 13.31% and 15.94%, respectively, over the baseline pseudo labels when using a magnesium crystal SR-CT sample. Additionally, we extensively evaluate the different components of our workflow including segmentation model, loss function, pseudo labeling strategy, and input type. Finally, we evaluate our approach on to two additional samples highlighting our frameworks ability to produce segmentations that are considerably better than the original pseudo labels.
Abstract (translated)
X射线计算机断层扫描（CT）是一种广泛使用的成像技术，可以详细检查物体的内部结构。同步加速器CT（SR-CT）通过使用高能单色X射线，能够提供改进的数据质量。虽然SR-CT允许更高的分辨率、时间解析实验以及减少成像伪影，但它也会生成比传统CT大得多的数据集。这些大数据集的准确和高效评估是工作流程中的关键组成部分；然而，在分析阶段通常手动进行，这代表了主要瓶颈之一。尽管深度学习已经作为一种能够提供广泛数据驱动解决方案的强大工具出现，它需要大量标注的数据来进行训练，并且在实践中对SR-CT数据集的手动注释几乎是不可行的。 本文介绍了一种新的框架，该框架能够在不需人工标记图像进行深度学习训练的情况下自动分割大规模、高分辨率的SR-CT数据集。首先，我们通过聚类体积中的像素值生成伪标签，识别具有相似衰减系数的区域，以产生初始语义图谱。之后，在伪标签上训练一个分割模型，并利用无偏教师方法自我纠正它们，确保最终分割准确。 我们的研究发现：在使用镁晶体SR-CT样本时，与基线伪标签相比，我们的方法分别提高了像素级精度13.31%和mIoU（平均交并比）15.94%。此外，我们还广泛评估了工作流程的不同组成部分，包括分割模型、损失函数、伪标记策略以及输入类型。最后，我们在两个额外样本上评估了我们的方法，突显了框架生成的分割结果优于原始伪标签的能力。 该研究提出的方法为大规模高分辨率SR-CT数据集提供了自动分割的可能性，并展示了深度学习技术在解决实际问题中的潜力和挑战。
URL
https://arxiv.org/abs/2603.00372
