Label Propagation Via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations

Anqi Feng, Yuan Xue, Yuli Wang, Chang Yan, Zhangxing Bian, Muhan Shao, Jiachen Zhuo, Rao P. Gullapalli, Aaron Carass, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Data-driven thalamic nuclei parcellation depends on highquality 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.

Original languageEnglish (US)
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
StatePublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: Apr 18 2023Apr 21 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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