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OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification

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Abstract

Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia, is the most reliable way to prevent oral cancer. Computational algorithms have been used as an auxiliary tool to aid specialists in this process. Usually, experiments are performed on private data, making it difficult to reproduce the results. There are several public datasets of histological images, but studies focused on oral dysplasia images use inaccessible datasets. This prevents the improvement of algorithms aimed at this lesion. This study introduces an annotated public dataset of oral epithelial dysplasia tissue images. The dataset includes 456 images acquired from 30 mouse tongues. The images were categorized among the lesion grades, with nuclear structures manually marked by a trained specialist and validated by a pathologist. Also, experiments were carried out in order to illustrate the potential of the proposed dataset in classification and segmentation processes commonly explored in the literature. Convolutional neural network (CNN) models for semantic and instance segmentation were employed on the images, which were pre-processed with stain normalization methods. Then, the segmented and non-segmented images were classified with CNN architectures and machine learning algorithms. The data obtained through these processes is available in the dataset. The segmentation stage showed the F1-score value of 0.83, obtained with the U-Net model using the ResNet-50 as a backbone. At the classification stage, the most expressive result was achieved with the Random Forest method, with an accuracy value of 94.22%. The results show that the segmentation contributed to the classification results, but studies are needed for the improvement of these stages of automated diagnosis. The original, gold standard, normalized, and segmented images are publicly available and may be used for the improvement of clinical applications of CAD methods on oral epithelial dysplasia tissue images.

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Data Availability

The data used in this study are openly available and can be used in other studies to evaluate the stages of OED analysis. The data can be acessed at: https://github.com/LIPAIGroup/OralEpitheliumDB.

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Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. The authors received financial support from National Council for Scientific and Technological Development CNPq (Grants #313643/2021-0, #311404/2021-9 and #307318/2022-2), the State of Minas Gerais Research Foundation - FAPEMIG (Grant #APQ-00578-18 and Grant #APQ-01129-21) and São Paulo Research Foundation - FAPESP (Grant #2022/03020-1).

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Authors and Affiliations

  1. Faculty of Computer Science (FACOM) - Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB, 38400-902, Uberlândia, MG, Brazil

    Adriano Barbosa Silva & Marcelo Zanchetta do Nascimento

  2. Federal Institute of Triângulo Mineiro (IFTM), R. Belarmino Vilela Junqueira, S/N, 38305-200, Ituiutaba, MG, Brazil

    Alessandro Santana Martins

  3. Science and Technology Institute, Federal University of São Paulo (UNIFESP), Av. Cesare Mansueto Giulio Lattes, 1201, 12247-014, São José dos Campos, SP, Brazil

    Thaína Aparecida Azevedo Tosta

  4. School of Dentistry, Federal University of Uberlândia (UFU), Av. Pará - 1720, 38405-320, Uberlândia, MG, Brazil

    Adriano Mota Loyola & Sérgio Vitorino Cardoso

  5. Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), R. Cristóvão Colombo, 2265, 38305-200, São José do Rio Preto, SP, Brazil

    Leandro Alves Neves

  6. Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Av. Amazonas, S/N, 38405-320, Uberlândia, MG, Brazil

    Paulo Rogério de Faria

Authors
  1. Adriano Barbosa Silva
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  2. Alessandro Santana Martins
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  3. Thaína Aparecida Azevedo Tosta
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  4. Adriano Mota Loyola
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  5. Sérgio Vitorino Cardoso
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  6. Leandro Alves Neves
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  7. Paulo Rogério de Faria
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  8. Marcelo Zanchetta do Nascimento
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Contributions

Adriano Barbosa Silva: conceptualization, investigation, methodology, software, validation, formal analysis, data curation, writing, visualization. Alessandro Santana Martins: investigation, validation, writing, visualization, resources, funding acquisition. Thaína Aparecida Azevedo Tosta: methodology, software, validation, writing, resources, funding acquisition. Adriano Mota Loyola: data curation, validation, resources, writing. Sérgio Vitorino Cardoso: data curation, validation, resources, writing. Leandro Alves Neves: validation, writing, resources, funding acquisition. Paulo Rogério de Faria: conceptualization, investigation, resources, data curation, validation, writing, supervision. Marcelo Zanchetta do Nascimento: conceptualization, methodology, software, validation, formal analysis, investigation, resources, writing, supervision, project administration, funding acquisition.

Corresponding author

Correspondence to Adriano Barbosa Silva.

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Ethics Approval

The experiments performed in this study were approved by the Ethics Committee on the Use of Animals under protocol numbers 038/09 and A016/21 at the Federal University of Uberlândia, Brazil.

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The authors declare no competing interests.

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Silva, A.B., Martins, A.S., Tosta, T.A.A. et al. OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01041-w

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