论文标题
CTA分割任务中无监督预训练的合成血管结构生成
Synthetic vascular structure generation for unsupervised pre-training in CTA segmentation tasks
论文作者
论文摘要
通常很难获得足够大的计算机断层扫描（CT）数据集来训练监督的深层模型。一个贡献的问题是创建地面真相标签的手动劳动数量，特别是用于体积数据。在这项研究中，我们在血管分割任务中训练U-NET架构，该架构可用于治疗中风患者时提供见解。我们创建了一个计算模型，该模型生成合成的血管结构，可以将其混合到头部未标记的CT扫描中。这种无监督的标记方法用于预先训练深度分割模型，与专门在手工标记的数据集中训练的模型相比，在真实示例中进行了微调，以提高准确性。
Large enough computed tomography (CT) data sets to train supervised deep models are often hard to come by. One contributing issue is the amount of manual labor that goes into creating ground truth labels, specially for volumetric data. In this research, we train a U-net architecture at a vessel segmentation task that can be used to provide insights when treating stroke patients. We create a computational model that generates synthetic vascular structures which can be blended into unlabeled CT scans of the head. This unsupervised approached to labelling is used to pre-train deep segmentation models, which are later fine-tuned on real examples to achieve an increase in accuracy compared to models trained exclusively on a hand-labeled data set.
