Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System

TL;DR

This paper introduces BSNet, a boundary guided semantic learning network that improves real-time COVID-19 lung infection segmentation from CT images by enhancing boundary detection and semantic feature integration.

Contribution

The paper proposes a novel dual-branch semantic enhancement module and a mirror-symmetric boundary guidance module for improved segmentation accuracy and boundary detection.

Findings

Outperforms state-of-the-art methods on public datasets.

Achieves real-time inference at 44 FPS.

Enhances boundary accuracy in infection segmentation.

Abstract

The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. Thanks to the development of deep learning technology, some deep learning solutions for lung infection segmentation have been proposed. However, due to the scattered distribution, complex background interference and blurred boundaries, the accuracy and completeness of the existing models are still unsatisfactory. To this end, we propose a boundary guided semantic learning network (BSNet) in this paper. On the one hand, the dual-branch semantic enhancement module that combines the top-level semantic preservation and progressive semantic integration is designed to model the complementary relationship between different high-level features, thereby promoting the generation of more complete segmentation results. On the other hand, the mirror-symmetric boundary guidance module is proposed to accurately detect the boundaries of the lesion regions in a mirror-symmetric way. Experiments on the publicly available dataset demonstrate that our BSNet outperforms the existing state-of-the-art competitors and achieves a real-time inference speed of 44 FPS.