A Multi-Scale Hyper Representation Network for Semantic Segmentation of Pulmonary Circulation Vessels in Pathology Assessment

Accurate segmentation of pulmonary vessels is essential for detailed analyses of the severity and progression of pulmonary hypertension. Microscopic image data from histolog-ical examinations are crucial for facilitating preclinical studies on the quantitative parameters of pulmonary circulation vessels. Inspired to accelerate the assessment of pulmonary vascular changes, which are associated with various diseases of different etiologies, this study proposes a novel segmentation approach based on a multi-scale hyper-convolution neural network. Our proposed network can segment pulmonary vessels effectively with a dice-coefficient score of 96.6 % on a standard dataset. Besides, this study also investigates the performance of other state-of-the-art segmentation networks to compare our approach with existing techniques. Our proposed method shows promising performance in terms of pixel-level accuracy compared to previous studies.