Semi-supervised learning has greatly advanced medical image segmentation
since it effectively alleviates the need of acquiring abundant annotations from
experts and utilizes unlabeled data which is much easier to acquire. Among
existing perturbed consistency learning methods, mean-teacher model serves as a
standard baseline for semi-supervised medical image segmentation. In this
paper, we present a simple yet efficient competitive ensembling teacher student
framework for semi-supervised for left atrium segmentation from 3D MR images,
in which two student models with different task-level disturbances are
introduced to learn mutually, while a competitive ensembling strategy is
performed to ensemble more reliable information to teacher model. Different
from the one-way transfer between teacher and student models, our framework
facilitates the collaborative learning procedure of different student models
with the guidance of teacher model and motivates different training networks
for a competitive learning and ensembling procedure to achieve better
performance. We evaluate our proposed method on the public Left Atrium (LA)
dataset and it obtains impressive performance gains by exploiting the unlabeled
data effectively and outperforms several existing semi-supervised methods.

本文提出了一种简单而高效的竞争集成教师学生框架，用于从 3D MR 图像中半监督地分割左心房，通过利用无标记数据并提供可靠信息给教师模型，以实现不同学生模型之间的协作学习并达到更好的性能。通过在公开 Left Atrium（LA）数据集上评估，我们的方法在有效利用无标记数据方面取得了显著的性能提升，并优于几种现有的半监督方法。