Pseudo-Label Assisted nnU-Net (PLAn) Enables Brain Segmentation at 7T

Motivation: Brain segmentation is more challenging at 7T compared to 3T, primarily due to increased bias fields and other artifacts. Generating training data for 7T brain segmentation is tedious, making transfer learning based models a more feasible option. Goal(s): Brain and lesion segmentation algorithm for use with 7T images in multiple sclerosis. Approach: A 3T to-7T transfer learning algorithm (called PLAn) for skull stripping, lesion, and brain segmentation was trained and tested on participants clinically diagnosed with multiple sclerosis. Results: In both quantitative and qualitative analysis, PLAn significantly outperformed other segmentation methods including nnU-Net in lesion and brain segmentation. Impact: Brain volume is a commonly used marker of disease progression in various neurological and neuropsychiatric diseases; however it is more difficult to implement on 7T images. PLAn, a deep-learning algorithm, can produce fast and reliable whole-brain segmentations.