In this paper, we present a new dynamic collaborative network for semi-supervised 3D vessel segmentation, termed DiCo. Conventional mean teacher (MT) methods typically employ a static approach, where the roles of the teacher and student models are fixed. However, due to the complexity of 3D vessel data, the teacher model may not always outperform the student model, leading to cognitive biases that can limit performance. To address this issue, we propose a dynamic collaborative network that allows the two models to dynamically switch their teacher-student roles. Additionally, we introduce a multi-view integration module to capture various perspectives of the inputs, mirroring the way doctors conduct medical analysis. We also incorporate adversarial supervision to constrain the shape of the segmented vessels in unlabeled data. In this process, the 3D volume is projected into 2D views to mitigate the impact of label inconsistencies. Experiments demonstrate that our DiCo method sets new state-of-the-art performance on three 3D vessel segmentation benchmarks. The code repository address is https://github.com/xujiaommcome/DiCo

翻译：本文提出了一种新的半监督三维血管分割动态协作网络，称为DiCo。传统的均值教师方法通常采用静态策略，其中教师模型和学生模型的角色是固定的。然而，由于三维血管数据的复杂性，教师模型可能并不总是优于学生模型，这会导致认知偏差，从而限制性能。为了解决这个问题，我们提出了一种动态协作网络，允许两个模型动态切换其师生角色。此外，我们引入了一个多视图集成模块，以捕获输入的不同视角，模拟医生进行医学分析的方式。我们还引入了对抗监督来约束未标记数据中分割血管的形状。在此过程中，三维体数据被投影为二维视图，以减轻标签不一致性的影响。实验表明，我们的DiCo方法在三个三维血管分割基准测试中取得了新的最先进性能。代码仓库地址为 https://github.com/xujiaommcome/DiCo