# Hyperspectral Image Segmentation: A Preliminary Study on the Oral and Dental Spectral Image Database (ODSI-DB)

arXiv: https://arxiv.org/abs/2303.08252 · HTML: https://mlsanity.com/paper/2303.08252/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Trends in deep learning for medical hyperspectral image analysis](https://mlsanity.com/paper/2011.13974/) (2020)

## Built on by (most cited first, 10 of 10)

- [A self-supervised and adversarial approach to hyperspectral demosaicking and RGB reconstruction in surgical imaging](https://mlsanity.com/paper/2407.19282/) (2024)
- [OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations](https://mlsanity.com/paper/2411.09553/) (2024)
- [MedicalNarratives: Connecting Medical Vision and Language with Localized Narratives](https://mlsanity.com/paper/2501.04184/) (2025)
- [Tree-based Semantic Losses: Application to Sparsely-supervised Large Multi-class Hyperspectral Segmentation](https://mlsanity.com/paper/2506.21150/) (2025)
- [Spectral Property-Driven Data Augmentation for Hyperspectral Single-Source Domain Generalization](https://mlsanity.com/paper/2603.16662/) (2026)
- [Towards Earlier Detection of Oral Diseases On Smartphones Using Oral and Dental RGB Images](https://mlsanity.com/paper/2308.15705/) (2023)
- [Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures](https://mlsanity.com/paper/2403.12050/) (2023)
- [A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver](https://mlsanity.com/paper/2411.06969/) (2024)
- [Label tree semantic losses for rich multi-class medical image segmentation](https://mlsanity.com/paper/2507.15777/) (2025)
- [Perspective-Equivariant Fine-tuning for Multispectral Demosaicing without Ground Truth](https://mlsanity.com/paper/2603.01332/) (2026)

## Abstract (arXiv)

Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapshot HSI cameras enable real-time imaging with significant potential for clinical applications. Despite this, the investigation into the relative performance of HSI over RGB imaging for semantic segmentation purposes has been limited, particularly in the context of medical imaging. Here we compare the performance of state-of-the-art deep learning image segmentation methods when trained on hyperspectral images, RGB images, hyperspectral pixels (minus spatial context), and RGB pixels (disregarding spatial context). To achieve this, we employ the recently released Oral and Dental Spectral Image Database (ODSI-DB), which consists of 215 manually segmented dental reflectance spectral images with 35 different classes across 30 human subjects. The recent development of snapshot HSI cameras has made real-time clinical HSI a distinct possibility, though successful application requires a comprehensive understanding of the additional information HSI offers. Our work highlights the relative importance of spectral resolution, spectral range, and spatial information to both guide the development of HSI cameras and inform future clinical HSI applications.