TY - GEN
T1 - Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT
AU - Wang, Ziyang
AU - Voiculescu, Irina
PY - 2021/12/9
Y1 - 2021/12/9
N2 - 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 inter-connections 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 segment of consolidation as well as glass, ground area via COVID-19 chest CT efficiently and accurately.
AB - 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 inter-connections 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 segment of consolidation as well as glass, ground area via COVID-19 chest CT efficiently and accurately.
KW - Computed Tomography
KW - COVID-19
KW - Image Segmentation
KW - Spatial Pyramid Network
UR - https://ieeexplore.ieee.org/document/9629904
UR - http://www.scopus.com/inward/record.url?scp=85122526590&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9629904
DO - 10.1109/EMBC46164.2021.9629904
M3 - Conference publication
C2 - 34891865
AN - SCOPUS:85122526590
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2956
EP - 2959
BT - 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PB - IEEE
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -