One-Shot Traumatic Brain Segmentation with Adversarial Training and Uncertainty Rectification

Brain segmentation of patients with severe traumatic brain injuries (sTBI) is essential for clinical treatment, but fully-supervised segmentation is limited by the lack of annotated data. One-shot segmentation based on learned transformations (OSSLT) has emerged as a powerful tool to overcome the limitations of insufficient training samples, which involves learning spatial and appearance transformations to perform data augmentation, and learning segmentation with augmented images. However, current practices face challenges in the limited diversity of augmented samples and the potential label error introduced by learned transformations. In this paper, we propose a novel one-shot traumatic brain segmentation method that surpasses these limitations by adversarial training and uncertainty rectification. The proposed method challenges the segmentation by adversarial disturbance of augmented samples to improve both the diversity of augmented data and the robustness of segmentation. Furthermore, potential label error introduced by learned transformations is rectified according to the uncertainty in segmentation. We validate the proposed method by the one-shot segmentation of consciousness-related brain regions in traumatic brain MR scans. Experimental results demonstrate that our proposed method has surpassed state-of-the-art alternatives. Code is available at https://github.com/hsiangyuzhao/TBIOneShot.