F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.179164.1 Data Note Articles Dataset of multi-focus (Z-stack) images derived from liquid-based cervical cancer cytology specimens

[version 1; peer review: awaiting peer review]

 Onishi Takafumi Data Curation Formal Analysis Investigation Methodology Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-2494-1265 a 1 2 Miyamoto Tomoyuki Data Curation Formal Analysis Investigation Methodology Resources Validation Visualization Writing – Review & Editing 3 4 Osawa Yukihiko Data Curation Formal Analysis Investigation Methodology Validation Visualization Writing – Review & Editing 1 2 Shibahara Kazuki Data Curation Formal Analysis Investigation Methodology Validation Visualization Writing – Review & Editing https://orcid.org/0009-0003-7060-5857 3 4 Nishimori Makoto Data Curation Formal Analysis Investigation Methodology Validation Visualization Writing – Review & Editing 3 4 Yakushiji Hiromasa Formal Analysis Investigation Methodology Validation Writing – Review & Editing https://orcid.org/0000-0003-1674-1359 3 4 Ohno Setsuyo Formal Analysis Methodology Resources Validation Visualization Writing – Review & Editing 2 Ohno Eiji Conceptualization Data Curation Formal Analysis Funding Acquisition Investigation Methodology Project Administration Resources Validation Visualization Writing – Review & Editing 2 Department of Medical Technology and Sciences, Faculty of Health Sciences, Kyoto Tachibana University, Kyoto, Kyoto, Japan Research Center for Life and Health Sciences, KyotoTachibana University, Kyoto, Kyoto, Japan Department of Medical Life Sciences, School of Medical Life Sciences, Kyushu University of Medical Science, Nobeoka, Miyazaki, Japan Cancer Cell Institute, Kyushu University of Medical Science, Nobeoka, Miyazaki, Japan onishi-ta@tachibana-u.ac.jp

No competing interests were disclosed.

 10 4 2026 2026 15 502 19 3 2026 Copyright: © 2026 Onishi T et al. 2026 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.

 Multi-focus imaging; Z-stack; Cervical cancer; Liquid-based cytology; Deep learning; Pap smear; cytology; cytopathology. JST CREST JPMJCR1786 JST CREST JPMJCR20F3 JST AIP Acceleration Research JPMJCR23U4 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. Introduction

Cervical cancer remains a leading cause of cancer-related deaths among women globally, and cytology is central to its early detection. 1 Liquid-based cytology (LBC) is a widely used standard method because of its specimen uniformity. Although image analysis using deep learning has flourished, existing public datasets such as the Herlev dataset, 2 Cervix93, 3 SIPaKMeD, 4 and CRIC Cervix 5 primarily consist of single-focus 2D static images. These do not fully reflect the actual diagnostic process in which cytotechnologists and pathologists integrate three-dimensional information by continuously adjusting the focus to evaluate chromatin structure, nuclear membrane irregularities, and cell overlapping. 6 Even with LBC, cells and nuclei retain a thickness of several to over 10 micrometers. Single-plane 2D images risk losing critical information, such as 3D nuclear morphology. Therefore, this dataset was constructed to provide multi-focus image sequences (Z-stacks) of cervical LBC specimens. By including continuous focal depths for each field of view, this dataset enables the development of analytical methods and artificial intelligence models that consider 3D morphological information, thus reflecting conditions closer to real-world clinical practice.

 Materials and methods

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.

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.

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.

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.

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.

Data description

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.

Dataset composition of the cervical cancer cell image database: Multi-focus cytology dataset.
Category Folder name No. of FOVs Images per FOV Total JPG files
Negative for intraepithelial lesion or malignancy NILM 273 11 3,003
Low-grade squamous intraepithelial lesion LSIL 93 11 1,023
High-grade squamous intraepithelial lesion HSIL 69 11 759
Squamous cell carcinoma and Adenocarcinoma SCC_etc 204 11 2,244
Total 639 7,029

Fields of view (FOVs), negative for intraepithelial lesion or malignancy (NILM), low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), squamous cell carcinoma (SCC_etc).

 Detailed cellular composition of the cervical cancer cell image database: Multi-focus cytology dataset.
Main folder name Subfolder name (cell type) No. of FOVs Images per FOV Total JPG files
NILM 1. Superficial-Intermediate Cells 30 11 330
2. Parabasal-Basal Cells 30 11 330
3. Glandular Cells (Isolated) 30 11 330
4. Glandular Cells (Cluster) 13 11 143
5. Squamous Metaplastic Cells 21 11 231
6. Repair Cells 6 11 66
7. Atrophic Vaginitis 21 11 231
8. Macrophages 91 11 1,001
9. Neutrophils 31 11 341
LSIL 10. Superficial-type Dysplastic Cells 11 11 121
11. Intermediate-type Dysplastic Cells 29 11 319
12. Superficial-type Koilocyte 32 11 352
13. Intermediate-type Koilocyte 21 11 231
HSIL 14. Deep-layer Dysplastic Cells 11 11 121
15. Carcinoma In Situ Cells 58 11 638
SCC_etc 16. Squamous Cell Carcinoma 175 11 1,925
17. Adenocarcinoma 29 11 319
Total 639 7,029

Fields of view (FOVs), negative for intraepithelial lesion or malignancy (NILM), low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), squamous cell carcinoma (SCC_etc).

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.

[Serial Number]: A unique sequential identifier assigned to each captured FOV.

[Center X, Center Y]: X and Y coordinates of the center of the FOV within the specimen (μm).

[Z-stack Index]: The focal plane number, ranging from 0 to 10 (representing 11 layers).

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.

The representative multi-focus (Z-stack) images for each category contained in the Cervical Cancer Cell Image Database: Multi-focus Cytology Dataset.

Negative for intraepithelial lesion or malignancy (NILM), low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), squamous cell carcinoma (SCC), and adenocarcinoma (AC).

 Value of the data

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.

 Limitations

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.

 Ethical considerations

In all cases, informed consent was obtained from the patients using an opt-out procedure. 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). All patient data were fully anonymized prior to the image extraction and annotation process to ensure the protection of personal information.

 Consent to publish

Cytology samples were used in anonymized form. Consent for the use of clinical samples and associated images for research and publication was obtained through an opt-out procedure approved by the institutional ethics committees, including the Ethics Committee of Kyushu University of Medical Science (Approval No. 17–19).

 Data availability

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:

CCCID_NILM_part1.zip.7z

This file contains image data for: 1. Superficial-Intermediate Cells, 2. Parabasal-Basal Cells, 3. Glandular Cells (Isolated), and 4. Glandular Cells (Cluster).

CCCID_NILM_part2.zip.7z

This file contains image data for: 5. Squamous Metaplastic Cells, 6. Repair Cells, 7. Atrophic Vaginitis, 8. Macrophages, and 9. Neutrophils.

CCCID_LSIL.zip.7z

This file contains image data for: 10. Superficial-type Dysplastic Cells, 11. Intermediate-type Dysplastic Cells, 12. Superficial-type Koilocyte, and 13. Intermediate-type Koilocyte.

CCCID_HSIL.zip.7z

This file contains image data for: 14. Deep-layer Dysplastic Cells and 15. Carcinoma in situ Cells.

CCCID_SCC_etc.zip.7z

This file contains image data for: 16. Squamous Cell Carcinoma and 17. Adenocarcinoma.

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

 Acknowledgements

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

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