POPSICLE: A Benchmark Suite for Machine Learning in Cryo-Electron Tomography
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[Submitted on 8 Jun 2026]
Summary
This article introduces POPSICLE, a benchmark suite for cryo-electron tomography (cryoET) segmentation and macromolecular localization. Built from the CryoET Data Portal, POPSICLE provides standardized, well-annotated benchmarks spanning eukaryotic and prokaryotic systems, purified and in situ samples, and both dense segmentation and sparse localization tasks. The authors note that ML development for cryoET has been bottlenecked by the lack of standardized benchmarks, and their baseline experiments show substantial variation in model rankings across tasks, highlighting the need for cryoET-specific evaluation practices rather than borrowing from adjacent biomedical imaging domains.
Source
bskyPOPSICLE: A Benchmark Suite for Machine Learning in Cryo-Electron Tomographyarxiv.orgKey quotes
· 5 pulledML 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 comparisons across methods.
Baseline experiments reveal substantial variation in model rankings across tasks, underscoring the need for benchmarks tailored to the unique characteristics of cryoET.
POPSICLE thus provides an open and extensible foundation for reproducible ML evaluation in cryoET.
Built on a living data resource, it can expand as new datasets and annotations become available.
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