Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation
This work addresses the challenge of accurate 3D vessel segmentation in medical imaging, which is crucial for diagnosis and treatment planning, representing an incremental improvement over existing methods.
The paper tackles the problem of semi-supervised 3D vessel segmentation by proposing a dynamic collaborative network (DiCo) that allows teacher and student models to switch roles dynamically, achieving state-of-the-art performance on three benchmarks.
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