¹ Department of Medical Technology and Sciences, Faculty of Health Sciences, Kyoto Tachibana University, Kyoto, Kyoto, Japan
² Research Center for Life and Health Sciences, KyotoTachibana University, Kyoto, Kyoto, Japan
³ Department of Medical Life Sciences, School of Medical Life Sciences, Kyushu University of Medical Science, Nobeoka, Miyazaki, Japan
⁴ Cancer Cell Institute, Kyushu University of Medical Science, Nobeoka, Miyazaki, Japan

Takafumi Onishi
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Tomoyuki Miyamoto
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing – Review & Editing

Yukihiko Osawa
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Review & Editing

Kazuki Shibahara
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Review & Editing

Makoto Nishimori
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – Review & Editing

Hiromasa Yakushiji
Roles: Formal Analysis, Investigation, Methodology, Validation, Writing – Review & Editing

Setsuyo Ohno
Roles: Formal Analysis, Methodology, Resources, Validation, Visualization, Writing – Review & Editing

Eiji Ohno
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Validation, Visualization, Writing – Review & Editing

• 1. Specimen Preparation and LBC Processing

  Cervical cytology specimens were collected from patients at Nobeoka Prefectural Hospital and Kawasaki Medical University. All samples were processed using the BD SurePath™ LBC system (BD Diagnostics, Burlington, NC, USA), which employs a density gradient enrichment process to provide a representative monolayer of cells. The processed slides were stained using the standard Papanicolaou staining method to visualize nuclear and cytoplasmic features. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Boards of all participating institutions, including the Ethics Committee of Kyushu University of Medical Science (Approval No. 17–19). Informed consent was obtained using an opt-out procedure approved by the ethics committees. The study included only adult participants, and all data were anonymized prior to image extraction and annotation. Written informed consent was waived in accordance with the opt-out procedure approved by the ethics committees, given the retrospective nature of the study and the use of fully anonymized samples.

• 2. Whole-slide imaging and Digital Acquisition

  The prepared LBC slides were digitized using a NanoZoomer-XR whole slide imaging scanner (Hamamatsu Photonics, Shizuoka, Japan). Scanning was performed using a 20× objective lens. The proprietary software of the scanner was used to generate whole-slide images in the .ndpi format.

• 3. Fields of view (FOVs) Selection and Multi-focus (Z-stack) Extraction

  Specific FOVs representing typical cytological features of each Bethesda category were identified from whole-slide images. For each selected FOV, a multi-focus image sequence (Z-stack) was generated. The extraction process involved capturing 11 distinct focal planes with a vertical interval of 1.0 μm between each plane. The focal range was centered on the optimal focus determined by the expert system, covering a total depth of 10 μm (5 μm above and below the center). The extracted images were cropped into 384 × 384 pixel tiles and converted from the raw.ndpi format to the JPG format with a resolution of 96 dpi for standardized use in machine-learning workflows.

• 4. Annotation and Consensus Validation

  The annotation of the dataset was conducted by six board-certified expert cytotechnologists. To facilitate the annotation process, a custom software, “Annotation Image Creation Tool,” developed by Proassist Ltd., (Osaka, Japan) was used. The annotation protocol was as follows. Each expert independently reviewed the multi-focus image sequences for each FOV. The FOVs were classified into one of the following four categories based on the Bethesda System: negative for intraepithelial lesion or malignancy (NILM), low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), squamous cell carcinoma (SCC). In this dataset, these are categorized as “SCC_etc,” which includes both SCC and adenocarcinoma (AC). A strict consensus rule was applied for inclusion in the final dataset; an FOV was included only if all six experts reached a 100% agreement on the diagnostic classification. The FOVs that did not achieve unanimous agreement were excluded from the dataset to ensure the highest possible ground-truth reliability.

• 5. Final Dataset Organization

  The final validated dataset, consisting of 639 FOVs (7,029 total images), was organized into the directory structure described in the Data description section. Each file was named according to its diagnostic class, FOV index, and Z-stack index to facilitate automated processing.

CCCID is organized into four main directories named according to the Bethesda System: “NILM,” “LSIL,” “HSIL,” and “SCC_etc” ( Table 1). Each directory contains subfolders corresponding to specific cellular or pathological types ( Table 2). The “NILM” directory contains nine subfolders representing normal or benign cellular components: 1. Superficial-Intermediate Cells, 2. Parabasal-Basal Cells, 3. Glandular Cells (Isolated), 4. Glandular Cells (Cluster), 5. Squamous Metaplastic Cells, 6. Repair Cells, 7. Atrophic Vaginitis, 8. Macrophages, and 9. Neutrophils. The “LSIL” directory contains four subfolders: 10. Superficial-type Dysplastic Cells, 11. Intermediate-type Dysplastic Cells, 12. Superficial-type Koilocyte, and 13. Intermediate-type Koilocyte. The “HSIL” directory contains two subfolders: 14. Deep-layer Dysplastic Cells and 15. Carcinoma in situ Cells. The “SCC_etc” directory contains two subfolders: 16. Squamous Cell Carcinoma and 17. Adenocarcinoma.

Each subfolder contains individual image files in JPG format, representing specific FOVs and their corresponding multi-focus planes.

The image files follow the naming convention: [Serial Number]_(CenterX, CenterY, Z-stack Index).jpg.

For example, the file “001_(3059,11012,0).jpg” represents the first captured image, located at specimen coordinates (3059 μm, 11012 μm), with a focal plane index of 0. The files “001_(3059,11012,0).jpg” through “001_(3059,11012,10).jpg” constitute the complete 11-layer Z-stack for the first FOV.

All images are 384 × 384 pixels with a resolution of 96 dpi. The Z-stack index 0 to 10 corresponds to a focal range captured at 1 μm intervals, providing a comprehensive volumetric view of the cellular and nuclear morphology. A representative image is shown in Figure 1.

CCCID provides unique multi-focus (Z-stack) image sequences of cervical cytology, addressing the limitations of existing single-focus 2D datasets by reflecting the actual diagnostic process used by cytotechnologists and cytopathologists. These data are valuable for developing and evaluating deep learning models that require three-dimensional morphological information, such as chromatin distribution and nuclear membrane irregularities, which are often blurred or lost in single-plane images. The dataset features high ground-truth reliability, as every included image was validated through 100% consensus among six expert cytotechnologists, minimizing interobserver variability in the training data. Researchers can reuse this dataset to benchmark computer-aided diagnosis systems, specifically for testing algorithms designed to handle overlapping cells or thick cell clusters common in LBC specimens. The inclusion of 11 focus layers at 1 μm intervals allows for the exploration of focus-fusion (Extended Depth of Field) algorithms and the study of how vertical focal shifts impact the accuracy of automated cell classification.

Although CCCID provides a high-quality multi-focus resource, several limitations should be noted. First, the data were collected using a specific Whole Slide Imaging scanner (NanoZoomer-XR) and a single LBC preparation method (BD SurePath™). Therefore, the visual characteristics of the images may differ from those produced by other scanners or preparation techniques, such as ThinPrep. Second, the dataset focuses on typical diagnostic images where six expert cytotechnologists reached a 100% consensus. This means that highly atypical or “borderline” cases, which often cause diagnostic disagreement in clinical practice, were intentionally excluded. Third, the number of FOVs varies across categories, resulting in class imbalance among the different cell types. Finally, the Z-stack range is fixed at 11 layers with 1 μm intervals.

Zenodo: Cervical Cancer Cell Image Database: Multi-focus Cytology Dataset (CCCID). https://doi.org/10.5281/zenodo.18904734.⁷

This project contains the following underlying data:

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

We thank Mr. Hideki Hashimoto of Proassist Ltd. for creating the Annotation Image Creation Tool used to construct this dataset.

• 1.  Giansanti D, Lastrucci A, Pirrera A, et al.: AI in cervical cancer cytology diagnostics: A narrative review of cutting-edge studies. Bioengineering (Basel). 2025; 12(7): 769. PubMed Abstract | Publisher Full Text | Free Full Text
• 2.  Jantzen J, Norup J, Dounias G, et al.: Pap-smear benchmark data for pattern classification, Nature Inspired Smart Information Systems (NiSIS 2005).2005; 1–9.
• 3.  Phoulady HA, Mouton PR: A new cervical cytology dataset for nucleus detection and image classification (Cervix93) and methods for cervical nucleus detection.2018. Publisher Full Text
• 4.  Plissiti ME, Dimitrakopoulos P, Sfikas G, et al.: Sipakmed: A new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images. 2018 25th IEEE International Conference on Image Processing (ICIP) IEEE; 2018; 3144–3148. Publisher Full Text
• 5.  Rezende MT, Silva R, Bernardo F d O, et al.: Cric searchable image database as a public platform for conventional Pap smear cytology data. Sci. Data 2021; 8(1): 151. PubMed Abstract | Publisher Full Text | Free Full Text
• 6.  Miyamoto T, Osawa Y, Nishimori M, et al.: Evaluating the efficacy of multi-focus images in digital cytopathology. Journal of health and welfare investigation. 2025; 9: 1–4. Publisher Full Text
• 7.  Ohno E, Ohno S, Miyamoto T, et al.: Cervical Cancer Cell Image Database: Multi-focus Cytology Dataset (CCCID) (1.0.0). [Data set]. Zenodo. 2026. Publisher Full Text

This work was supported by JST CREST Grant Number JPMJCR1786, JST CREST Grant Number JPMJCR20F3, and JST AIP Acceleration Research Grant Number JPMJCR23U4.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Current Reviewer Status: ?

VERSION 1

PUBLISHED 10 Apr 2026

Reviewer Report 08 Aug 2026

Nisreen Albzour, Binghamton University, Binghamton, USA 

Approved

VIEWS 0

Reviewer Report 25 Jun 2026

Vedrana Ivezic, University of California Los Angeles, Los Angeles, California, USA 

Approved

VIEWS 0