动态切换师生角色，提升3D血管分割精度
Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation
- 师生模型可动态互换角色，避免固定分工偏差
- 在三个基准上达到最新最好效果，最高达94.7% Dice
- 结合多视角融合与对抗监督，适合医学影像分析
本文提出一种用于半监督3D血管分割的动态协同网络DiCo。传统均值教师（MT）方法采用固定师生角色，但面对复杂的3D血管数据时，教师模型未必优于学生模型，易引入认知偏差。为此，我们设计动态协作机制，使师生角色可动态切换。同时引入多视角融合模块，模拟医生从不同角度分析影像；通过将3D体积投影至2D视图，结合对抗监督约束未标记数据中血管形状，缓解标签不一致问题。实验表明，DiCo在三个3D血管分割基准上均取得新最优性能，最高Dice系数达94.7%。代码已开源：https://github.com/xujiaommcome/DiCo。
原文摘要 · Abstract (English)
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
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