POPSICLE: Benchmark Datasets for Segmentation and Localization in CryoET
Cryo-electron tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact cells, thereby linking molecular architecture to cellular organization in a native context. Realizing the full potential of cryoET, however, increasingly depends on advances in computational analysis, particularly machine learning (ML), to interpret its complex and information-rich data. Despite rapid progress, ML development for cryoET remains bottlenecked by the lack of standardized, well-annotated benchmarks. Existing evaluations are typically small, task-specific, and are assembled in isolation, limiting robust ...
| Authors | Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens et al. |
|---|---|
| Primary category | eess.IV |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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