Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation
This work addresses the problem of reducing expert annotation needs for medical image segmentation, specifically for left atrium MRI, but it is incremental as it builds on existing perturbed consistency learning methods.
The paper tackles semi-supervised segmentation of the left atrium in 3D MRI by proposing a competitive ensembling teacher-student framework, resulting in impressive performance gains that outperform existing methods on a public dataset.
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.