用少量标注训练3D血管壁分割，提升动脉狭窄评估效率
Learning Wall Segmentation in 3D Vessel Trees using Sparse Annotations
- 用中心线采样垂直截面，2D对抗网络分割后转为3D伪标签
- 在分叉区沿分叉轴采样截面，分割准确率显著提升
- 仅需稀疏标注即可训练，适合临床3D生物标志物提取
提出一种新方法，利用临床研究中的稀疏标注训练颈动脉壁的3D分割。通过中心线采样垂直于血管的截面，使用对抗性2D网络进行分割，并将结果转化为3D伪标签，用于3D卷积神经网络训练，避免手动绘制3D掩码。针对分叉区域，提出沿分叉轴生成垂直截面，显著提升分割性能；不同采样距离影响较小。该方法可高效训练3D分割模型，有望改进颈动脉狭窄评估，并支持提取如斑块体积等3D生物标志物。
原文摘要 · Abstract (English)
We propose a novel approach that uses sparse annotations from clinical studies to train a 3D segmentation of the carotid artery wall. We use a centerline annotation to sample perpendicular cross-sections of the carotid artery and use an adversarial 2D network to segment them. These annotations are then transformed into 3D pseudo-labels for training of a 3D convolutional neural network, circumventing the creation of manual 3D masks. For pseudo-label creation in the bifurcation area we propose the use of cross-sections perpendicular to the bifurcation axis and show that this enhances segmentation performance. Different sampling distances had a lesser impact. The proposed method allows for efficient training of 3D segmentation, offering potential improvements in the assessment of carotid artery stenosis and allowing the extraction of 3D biomarkers such as plaque volume.
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