In-vivo non-contact multispectral oral disease image dataset with segmentation

• Published in: Scientific data Vol. 11; no. 1; pp. 1298 - 9
• Main Authors: Chand, Sneha, Namasivayam, Karthik, Dave, Janak, Preejith, S. P., Jayachandran, Sadaksharam, Sivaprakasam, Mohanasankar
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/245/2226; 631/67/1665/3016; 631/67/2321; 692/699/3020/1665/3016; Artificial intelligence; Carcinoma, Squamous Cell - diagnostic imaging; Data Descriptor; Datasets; Fibrosis; Humanities and Social Sciences; Humans; Image processing; Leukokeratosis; Leukoplakia, Oral - diagnostic imaging; Mouth Diseases - diagnosis; Mouth Diseases - diagnostic imaging; Mouth Neoplasms - diagnostic imaging; multidisciplinary; Oral cancer; Oral carcinoma; Oral diseases; Oral hygiene; Oral squamous cell carcinoma; Oral Submucous Fibrosis - diagnostic imaging; Science; Science (multidisciplinary); Spectroscopy; Spectrum Analysis; Squamous cell carcinoma
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-024-04099-x

In imaging spectroscopy, gathering oral tissue spectral data from resected samples may not accurately represent tissue signatures due to time-dependent changes, blood loss, protein degeneration, and preservation chemicals. In-vivo spectral imaging is employed to address these limitations, but it poses challenges like device dimensions, tissue accessibility, and motion artifacts, impacting data quality and reliability. Our study publishes a dataset of spectral images focusing on oral diseases, addressing these challenges. We used a state-of-the-art multispectral camera, capturing images at 270*510 pixels resolution in 16 spectral bands (460 nm to 600 nm). The dataset includes 91 participants (15 healthy and 76 diseased), with multiple images per patient, totalling 243 spectral images. The dataset encompasses three oral health conditions: Oral Submucous Fibrosis (OSMF), Leukoplakia, and Oral Squamous Cell Carcinoma (OSCC). Detailed patient history records accompany each case. This publicly available oral health multispectral dataset has the potential to advance spectroscopy diagnosis. Integrating artificial intelligence with a comprehensive spectral signature repository holds promise for accurate disease analysis.