  Data-driven thalamic nuclei parcellation depends on high-quality manual
annotations. However, the small size and low contrast changes among thalamic
nuclei, yield annotations that are often incomplete, noisy, or ambiguously
labelled. To train a robust thalamic nuclei parcellation model with noisy
annotations, we propose a label propagation algorithm based on random walker to
refine the annotations before model training. A two-step model was trained to
generate first the whole thalamus and then the nuclei masks. We conducted
experiments on a mild traumatic brain injury~(mTBI) dataset with noisy thalamic
nuclei annotations. Our model outperforms current state-of-the-art thalamic
nuclei parcellations by a clear margin. We believe our method can also
facilitate the training of other parcellation models with noisy labels.
