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.
@article{arxiv.2303.17706,
title = {Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations},
author = {Anqi Feng and Yuan Xue and Yuli Wang and Chang Yan and Zhangxing Bian and Muhan Shao and Jiachen Zhuo and Rao P. Gullapalli and Aaron Carass and Jerry L. Prince},
journal= {arXiv preprint arXiv:2303.17706},
year = {2023}
}