用插入网络提升2D影像序列定位精度，更准地找到3D扫描中的关键切片。
Insertion Network for Image Sequence Correspondence
- 通过学习将切片插入另一序列的合适位置来建立影像序列对应关系。
- 在监督设置下切片定位误差从8.4毫米降至5.4毫米，显著提升准确率。
- 适合需要精准切片定位的医学影像分析任务，如自动配准与分割前处理。
我们提出一种新方法，用于建立两组2D图像序列之间的对应关系。该技术的一个重要应用是切片级内容导航，目标是定位3D体数据中的特定2D切片，或根据2D切片确定3D扫描的解剖覆盖范围。这在多种诊断任务及自动配准与分割流程中是关键预处理步骤。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的合适位置来构建序列对应关系。具体通过编码每张切片的上下文表示，并使用切片间注意力机制建模插入过程实现。我们将该方法应用于人体CT扫描中人工标记的关键切片定位任务，并与当前最先进的体部回归方法（body part regression）进行比较。后者独立处理每张切片，而我们的方法利用整个序列的上下文信息。实验结果表明，在监督设置下，插入网络将切片定位误差从8.4毫米降低至5.4毫米，显著提升了定位精度。
原文摘要 · Abstract (English)
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.
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