ArticleF1000Research2026
Dataset of multi-focus (Z-stack) images derived from liquid-based cervical cancer cytology specimens.
Article in F1000Research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Background: In cervical cancer screening, cytotechnologists and cytopathologists integrate three-dimensional information by continuously adjusting the microscope's focus to evaluate chromatin structure and nuclear morphology. However, most existing public datasets consist of single-focus 2D images, which do not fully reflect this clinical diagnostic workflow. This study presents the Cervical Cancer Cell Image Database: Multi-focus Cytology Dataset (CCCID) to bridge this gap. Methods: Cervical specimens were processed using the BD SurePath™ LBC technique and Papanicolaou staining. Digitization was performed using a NanoZoomer-XR scanner. For 639 unique fields of view (FOVs), a Z-stack consisting of 11 focal planes was captured at 1.0 μm intervals, resulting in 7,029 images (384 × 384 pixels). Ground-truth labels were established only when six board-certified expert cytotechnologists reached 100% consensus. Conclusions: The CCCID provides a high-reliability benchmark for developing machine-learning models that utilize axial (Z-axis) information. It is highly valuable for advancing three-dimensional nuclear morphology analysis, cell segmentation in overlapping clusters, and the evaluation of focus-fusion algorithms in digital cytopathology.
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