改进血管气管中心线追踪，解决分支重复和提前终止问题
Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes
- 引入拓扑修正机制，提升3D医学图像中管状结构追踪精度
- 在三个新构建数据集上超越现有最先进模型，尤其在真实数据表现突出
- 适合医学影像分析、智能诊断等需要精确解剖结构建模的场景
管状树状结构（如血管、气道）在人体解剖中至关重要，准确追踪并保持其拓扑结构对下游任务极为关键。Trexplorer 是一种用于3D医学图像中心线追踪的递归模型，但存在预测重复分支和过早终止追踪的问题。为此，我们提出 Trexplorer Super，通过新方法显著提升性能。由于缺乏公开数据集，评估中心线追踪模型极具挑战。为此，我们构建了三个中心线数据集——一个合成数据集和两个真实数据集，难度逐级递增。基于这些数据集，我们对现有最先进（SOTA）模型进行全面评估，并与本方法对比。结果表明，Trexplorer Super 在所有数据集上均优于先前 SOTA 模型。此外，研究发现：在合成数据上表现优异并不意味着在真实数据上同样有效。代码与数据集已开源于 https://github.com/RomStriker/Trexplorer-Super。
原文摘要 · Abstract (English)
Tubular tree structures, such as blood vessels and airways, are essential in human anatomy and accurately tracking them while preserving their topology is crucial for various downstream tasks. Trexplorer is a recurrent model designed for centerline tracking in 3D medical images but it struggles with predicting duplicate branches and terminating tracking prematurely. To address these issues, we present Trexplorer Super, an enhanced version that notably improves performance through novel advancements. However, evaluating centerline tracking models is challenging due to the lack of public datasets. To enable thorough evaluation, we develop three centerline datasets, one synthetic and two real, each with increasing difficulty. Using these datasets, we conduct a comprehensive evaluation of existing state-of-the-art (SOTA) models and compare them with our approach. Trexplorer Super outperforms previous SOTA models on every dataset. Our results also highlight that strong performance on synthetic data does not necessarily translate to real datasets. The code and datasets are available at https://github.com/RomStriker/Trexplorer-Super.
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