The rise of deep learning has introduced a transformative era in the field of
image processing, particularly in the context of computed tomography. Deep
learning has made a significant contribution to the field of industrial
Computed Tomography. However, many defect detection algorithms are applied
directly to the reconstructed domain, often disregarding the raw sensor data.
This paper shifts the focus to the use of sinograms. Within this framework, we
present a comprehensive three-step deep learning algorithm, designed to
identify and analyze defects within objects without resorting to image
reconstruction. These three steps are defect segmentation, mask isolation, and
defect analysis. We use a U-Net-based architecture for defect segmentation. Our
method achieves the Intersection over Union of 92.02% on our simulated data,
with an average position error of 1.3 pixels for defect detection on a
512-pixel-wide detector.

该论文介绍了深度学习在计算机断层成像领域的应用，提出了一种基于三步深度学习算法的综合缺陷检测方法，包括缺陷分割、蒙版分离和缺陷分析。使用 U-Net 架构进行缺陷分割，在模拟数据上达到 92.02％的 IoU，并在 512 像素宽的探测器上实现了 1.3 像素的平均位置误差。

基于二维正弦图的计算机断层成像中的缺陷定位方法

A 2D Sinogram-Based Approach to Defect Localization in Computed  Tomography

This work reports the empirical performance of an automated medical landmark
detection method for predict clinical markers in hip radiograph images.
Notably, the detection method was trained using a label-only augmentation
scheme; our results indicate that this form of augmentation outperforms
traditional data augmentation and produces highly sample efficient estimators.
We train a generic U-Net-based architecture under a curriculum consisting of
two phases: initially relaxing the landmarking task by enlarging the label
points to regions, then gradually eroding these label regions back to the base
task. We measure the benefits of this approach on six datasets of radiographs
with gold-standard expert annotations.

本文通过在髋部 X 光图像中自动检测医学标志物的实证表现，报道了一种预测临床标记物的自动化医学标志物检测方法。结果显示，该标志物检测方法使用纯标签增强方案进行训练，优于传统的数据增强方法并产生高度样本效率的估计器。我们通过使用基于通用 U-Net 架构的课程进行训练，逐步放宽通过将标签点扩大为区域来定位标志物的任务，并逐渐侵蚀这些标签区域回到基本任务。我们使用六个具有金标准专家注释的放射图像数据集来评估该方法的效益。