Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT

TL;DR

This paper introduces QAP-Net, a quadruple augmented pyramid network designed for multi-class COVID-19 lung lesion segmentation in CT scans, improving accuracy and efficiency over existing methods.

Contribution

The paper proposes a novel quadruple augmented pyramid network that enhances feature extraction and transfer for multi-class COVID-19 CT segmentation.

Findings

Achieved Dice score of 0.8163, outperforming state-of-the-art methods.

Effectively segments consolidation, glass, and ground area in COVID-19 CT images.

Demonstrates high accuracy and efficiency in multi-class lung lesion segmentation.

Abstract

COVID-19, a new strain of coronavirus disease, has been one of the most serious and infectious disease in the world. Chest CT is essential in prognostication, diagnosing this disease, and assessing the complication. In this paper, a multi-class COVID-19 CT segmentation is proposed aiming at helping radiologists estimate the extent of effected lung volume. We utilized four augmented pyramid networks on an encoder-decoder segmentation framework. Quadruple Augmented Pyramid Network (QAP-Net) not only enable CNN capture features from variation size of CT images, but also act as spatial interconnections and down-sampling to transfer sufficient feature information for semantic segmentation. Experimental results achieve competitive performance in segmentation with the Dice of 0.8163, which outperforms other state-of-the-art methods, demonstrating the proposed framework can segments of consolidation as well as glass, ground area via COVID-19 chest CT efficiently and accurately.