OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification.
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 diffi...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1691 - 1711 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179554132&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554132 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554132 179554132 179554132 10.1007/s10278-024-01041-w 179554132 ppf: 1691 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification. aug: au: Silva, Adriano Barbosa Martins, Alessandro Santana Tosta, Thaína Aparecida Azevedo Loyola, Adriano Mota Cardoso, Sérgio Vitorino Neves, Leandro Alves de Faria, Paulo Rogério do Nascimento, Marcelo Zanchetta affil: Faculty of Computer Science (FACOM) - Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB, 38400-902, Uberlândia, MG, Brazil sug: subj: Image Interpretation, Computer Assisted Data Management Epithelium Pathology Mouth Neoplasms Diagnosis Mouth Neoplasms Classification Precancerous Conditions Diagnosis Human Funding Source Neoplasm Grading Pathologists Neural Networks (Computer) Image Processing, Computer Assisted Machine Learning Algorithms Random Forest Validity Automation ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|