Cerebrovascular segmentation from Time-of-flight magnetic resonance angiography (TOF-MRA) is a critical step in computer-aided diagnosis. In recent years, deep learning models have proved its powerful feature extraction for cerebrovascular segmentation. However, they require many labeled datasets to implement effective driving, which are expensive and professional. In this paper, we propose a generative consistency for semi-supervised (GCS) model. Considering the rich information contained in the feature map, the GCS model utilizes the generation results to constrain the segmentation model. The generated data comes from labeled data, unlabeled data, and unlabeled data after perturbation, respectively. The GCS model also calculates the consistency of the perturbed data to improve the feature mining ability. Subsequently, we propose a new model as the backbone of the GSC model. It transfers TOF-MRA into graph space and establishes correlation using Transformer. We demonstrated the effectiveness of the proposed model on TOF-MRA representations, and tested the GCS model with state-of-the-art semi-supervised methods using the proposed model as backbone. The experiments prove the important role of the GCS model in cerebrovascular segmentation. Code is available at https://github.com/MontaEllis/SSL-For-Medical-Segmentation .

中文翻译：

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TOF-MRA 半监督脑血管分割的生成一致性

飞行时间磁共振血管造影（TOF-MRA）的脑血管分割是计算机辅助诊断的关键步骤。近年来，深度学习模型证明了其在脑血管分割方面强大的特征提取能力。然而，他们需要许多标记数据集来实现有效的驾驶，这是昂贵且专业的。在本文中，我们提出了半监督（GCS）模型的生成一致性。考虑到特征图中包含的丰富信息，GCS模型利用生成结果来约束分割模型。生成的数据分别来自标记数据、未标记数据和扰动后的未标记数据。GCS模型还计算扰动数据的一致性，以提高特征挖掘能力。随后，我们提出了一个新模型作为 GSC 模型的骨干。它将 TOF-MRA 传输到图空间并使用 Transformer 建立关联。我们证明了所提出的模型在 TOF-MRA 表示上的有效性，并使用所提出的模型作为骨干，使用最先进的半监督方法测试了 GCS 模型。实验证明了GCS模型在脑血管分割中的重要作用。代码可在https://github.com/MontaEllis/SSL-For-Medical-Segmentation 。