MSN-TCSeg: A transcranial sonography dataset and benchmarking for midbrain and substantia nigra hyperechogenicity segmentation.

BACKGROUND AND OBJECTIVES: In transcranial sonography (TCS), Parkinson's disease (PD) patients often show substantia nigra hyperechogenicity (SN+), which is currently the most reliable imaging biomarker for early PD diagnosis through TCS. The quantitative assessment of both SN+ and midbrain is important for early PD detection. However, current segmentation methods for SN+ and midbrain depend on manual delineation and small, non-public datasets, limiting reproducibility and fair comparison of automatic segmentation techniques. METHODS: TCS images were retrospectively collected and underwent screening, standardization, and de-identification. Two experienced neurosonologists independently annotated midbrain and SN+ regions under a double-blind protocol. Inter-observer agreement was evaluated, and target-specific procedures were used to construct gold standard labels. A third neurosonologist independently annotated the SN+ subset, and probabilistic soft labels were generated from the three annotations. Fourteen representative models from six segmentation paradigms were evaluated through five fold cross validation at the subject level using unified region and boundary metrics. RESULTS: The resulting MSN-TCSeg dataset comprised 700 TCS images with pixel-level midbrain annotations and 370 TCS images with pixel-level SN+ annotations, with probabilistic soft labels additionally provided for SN+ subset. Inter-observer agreement was substantially higher for the midbrain than for SN+, confirming greater annotation uncertainty of SN+. Across the 14 benchmark models, midbrain segmentation achieved consistently higher accuracy and lower boundary errors, whereas SN+ segmentation remained considerably more challenging. CONCLUSIONS: MSN-TCSeg provide a standardized reference platform for automated TCS analysis. They are expected to promote the development and objective evaluation of algorithms for large-scale quantification of PD-related imaging biomarkers.