Tongue Tumor Detection in Hyperspectral Images Using Deep Learning Semantic Segmentation

Conclusion: On a clinical data set with tongue squamous cell carcinoma, our best method obtains very strong results of average dice coefficient, and area under the ROC-curve of $text{0.891}pm text{0.053}$, and $text{0.924}pm text{0.036}$, respectively on the original spatial image size. The results show that a very good performance can be achieved even with a limited amount of data. We demonstrate that important information regarding tumor decision is encoded in various channels, but some channel selection, and filtering is beneficial over the full spectra. Moreover, we use both visual (VIS), and near-infrared (NIR) spectrum, rather than commonly used only VIS spectrum; although VIS spectrum is generally of higher significance, we demonstrate NIR spectrum is crucial for tumor capturing in some cases. Significance: The HSI technology augmented with accurate deep learning algorithms has a huge potential to be a promising alternative to digital pathology or a doctors’ supportive tool in real-time surgeries.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research