Comparative Study on Medical Hyperspectral Images Segmentation Using Advanced Deep Learning Techniques

Accurate medical image segmentation is essential for disease diagnosis, treatment planning, and surgery guidance. Hyperspectral imaging (HSI) improves medical imaging by providing rich spectral information, leading to improved tissue classification, cancer detection, and visualization of anatomical structures. Although deep learning-based segmentation models like U-Net and SegNet have advanced hyperspectral medical image analysis, they still face challenges in effectively handling spectral-spatial relationships. Recent advanced models, such as spectral transformers, knowledge distillation models (KDM), and dual-stream architecture, attempt to address these issues. However, there is a lack of comparative evaluation of these models on diverse medical hyperspectral datasets, making it difficult to determine the most effective approach. To address this gap, this study conducts a comprehensive evaluation of deep learning-based segmentation models, analyzing their performance across multiple medical hyperspectral datasets. Three different medical HSI datasets were collected: oral and dental, pathology, and brain datasets to evaluate three deep learning models designed to overcome specific segmentation challenges: long-distance dependency handling, and high spectral and spatial redundancy. The selected models were trained and tested on the three datasets and evaluated using the two metrics: the Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). The results indicate that KDM achieves the best performance in the pathology dataset, while as the Dual-Stream with a Feature Pyramid Network [FPN] spatial backbone performs well in the oral and dental data sets, and the Dual-Stream with a DeepLab spatial backbone excels in the brain data set. These findings provide valuable insights into model suitability for different hyperspectral medical imaging applications.Clinical relevance- This study provides valuable insights on the importance of the medical hyperspectral imaging by identifying optimal segmentation models for pathology, oral and dental, and brain imaging, improving disease diagnosis and treatment planning.