Automatic tissue segmentation of hyperspectral images in liver and head neck surgeries using machine learning

Aim: Proper identification in real time of different types of tissues during intraoperative procedures represents a vital and challenging task. This paper addresses tissue segmentation in two different medical applications using hyperspectral imaging (HSI) and machine learning in two main steps.Methods: The first step consists of data preprocessing designed to overcome the most common problems linked with HSI, involving inter- and intra-patient variability of the tissue spectra and the high dimensionality of the spectra. The preprocessing step involves outlier removal, spectral smoothing, normalization, and dimensionality reduction using principal component analysis applied in the spectral domain of HSI data. In the spatial domain, multiple levels of analysis are performed using Gaussian filters. The second step consists of tissue segmentation using an optimized machine learning model. The most suitable model was selected under statistical comparison of seven machine learning models involving three different levels of spatial analysis.Results: According to the experimental results, the U-Net achieves the highest precision (0.908) for detection of liver, bile duct, artery, and portal vein tissues in a set of 18 HSI data, while the logistic regression with the elasticnet regularization combined with multiscale spatial analysis obtains the highest F1-score (0.673) and segmentation accuracy (0.803) for thyroid and parathyroid glands segmentation in a set of 21 HSI data.Conclusion: In addition to the computational experiments, combining machine learning with HSI represents a promising approach to perform image-guided surgery