Comparative Analysis of Deep Learning Methods for Classification of Ablated Regions in Hyperspectral Images of Atrial Tissue - Yeva Gabrielyan; Arpi Hunanyan; Sona Bezirganyan; Lusine Davtyan; Ani Avetisyan; Narine Sarvazyan; Aram Butavyan; Varduhi Yeghiazaryan (IEEE Access)
Radiofrequency ablation (RFA) is used to treat atrial fibrillation (AF). Viability gaps between ablated regions can lead to AF recurrence; thus, correctly detecting RFA lesions is important for successful treatment. Hyperspectral imaging (HSI) has been previously shown to aid in visualizing RFA-affected areas. However, automated classification of ablated/unabled tissue in HSI remains open. We comparatively analyze state-of-the-art deep learning models for HSI classification from the field of remote sensing. We group them based on model architecture and utilization of spectral and spatial contexts. We deploy a pre-clinical HSI dataset obtained from excised left atrial tissue of four porcine and five bovine samples. The 45 classification algorithms are benchmarked through two types of experiments: objective and subjective. For objective evaluation, per HSI cube, we limit the training and testing to rectangular areas of known classes (ablated/unablated), as reliable classification masks for whole HSI cubes are not available. For both porcine and bovine data, models incorporating graph neural networks demonstrate superior performance. The best eight algorithms are further compared in a subjective experiment. We ask seventeen participants to rate RGB visualizations of model outputs. In this experiment, the performance of an algorithm is assessed on the whole HSI cube, and is subject to participants’ interpretation of the extent of RFA-affected tissue. Our comparative analysis allows us to observe the suitability of different deep learning architectures for HSI classification of affected tissue. These efforts contribute towards the potential future utilization of HSI for surgical guidance in RFA or similar procedures.