图像序列对应性插入网络
Insertion Network for Image Sequence Correspondence
AI总结：
该研究提出了一种基于插入网络的图像序列对应方法，用于提高三维体积中切片定位的准确性，通过上下文信息建模实现比现有方法更精确的切片定位。
摘要：
我们提出了一种新颖的方法，用于建立两个二维图像序列之间的对应关系。这一技术的一个特定应用是切片级内容导航，其目标是在三维体积中定位特定的二维切片，或根据其二维切片确定三维扫描的解剖覆盖范围。这为各种诊断任务以及自动配准和分割流程提供了重要的预处理步骤。我们的方法通过训练网络学习如何将一个序列中的切片插入到另一个序列中的适当位置来建立序列对应关系。这通过编码每个切片的上下文表示，并利用切片到切片的注意力机制来建模插入过程来实现。我们将这种方法应用于定位手动标注的关键切片，并将其性能与当前最先进的替代方法——身体部分回归进行比较，后者为每个切片预测解剖位置评分。与身体部分回归不同，我们的方法利用了整个序列的上下文信息。实验结果表明，在监督设置中，插入网络将切片定位误差从8.4毫米减少到5.4毫米，显示出在准确性上的显著提升。
英文摘要：
We propose a novel method for establishing correspondence between two sequences of 2D images. One particular application of this technique is slice-level content navigation, where the goal is to localize specific 2D slices within a 3D volume or determine the anatomical coverage of a 3D scan based on its 2D slices. This serves as an important preprocessing step for various diagnostic tasks, as well as for automatic registration and segmentation pipelines. Our approach builds sequence correspondence by training a network to learn how to insert a slice from one sequence into the appropriate position in another. This is achieved by encoding contextual representations of each slice and modeling the insertion process using a slice-to-slice attention mechanism. We apply this method to localize manually labeled key slices in body CT scans and compare its performance to the current state-of-the-art alternative known as body part regression, which predicts anatomical position scores for individual slices. Unlike body part regression, which treats each slice independently, our method leverages contextual information from the entire sequence. Experimental results show that the insertion network reduces slice localization errors in supervised settings from 8.4 mm to 5.4 mm, demonstrating a substantial improvement in accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
