TY - GEN
T1 - Two-Stage Mapping-Segmentation Framework for Delineating COVID-19 Infections from Heterogeneous CT Images
AU - Li, Tong
AU - Wang, Zhuochen
AU - Chen, Yanbo
AU - Zhang, Lichi
AU - Gao, Yaozong
AU - Shi, Feng
AU - Qian, Dahong
AU - Wang, Qian
AU - Shen, Dinggang
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Infection segmentation is essential for quantitative assessment in computer-aided management of COVID-19. However, clinical CT images are usually heterogeneous, which are reconstructed by different protocols with varying appearance and voxel spacing due to radiologist preference. Most existing infection segmentation models are only trained using specific types of CT images, which would undermine the performance when applied to other types of CT images. Therefore, it is highly desirable to construct a model that could be applied to heterogeneous CT images in the urgent COVID-19 applications. In this paper, we present a two-stage mapping-segmentation framework for delineating COVID-19 infections from CT images. To compensate for heterogeneity of CT images obtained from different imaging centers, we develop an image-level domain-adaptive process to transform all kinds of images into a target type, and then segment COVID-19 infections accordingly. Experiments show that the infection delineation performance based on our proposed method is superior to the model trained jointly using mixture of all types of images, and is also comparable to those models supervised by using only specific types of images (if applicable).
AB - Infection segmentation is essential for quantitative assessment in computer-aided management of COVID-19. However, clinical CT images are usually heterogeneous, which are reconstructed by different protocols with varying appearance and voxel spacing due to radiologist preference. Most existing infection segmentation models are only trained using specific types of CT images, which would undermine the performance when applied to other types of CT images. Therefore, it is highly desirable to construct a model that could be applied to heterogeneous CT images in the urgent COVID-19 applications. In this paper, we present a two-stage mapping-segmentation framework for delineating COVID-19 infections from CT images. To compensate for heterogeneity of CT images obtained from different imaging centers, we develop an image-level domain-adaptive process to transform all kinds of images into a target type, and then segment COVID-19 infections accordingly. Experiments show that the infection delineation performance based on our proposed method is superior to the model trained jointly using mixture of all types of images, and is also comparable to those models supervised by using only specific types of images (if applicable).
KW - COVID-19
KW - Domain-adaption
KW - Image segmentation
UR - http://www.scopus.com/inward/record.url?scp=85097257561&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62469-9_1
DO - 10.1007/978-3-030-62469-9_1
M3 - Conference contribution
AN - SCOPUS:85097257561
SN - 9783030624682
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 13
BT - Thoracic Image Analysis - Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Petersen, Jens
A2 - San José Estépar, Raúl
A2 - Schmidt-Richberg, Alexander
A2 - Gerard, Sarah
A2 - Lassen-Schmidt, Bianca
A2 - Jacobs, Colin
A2 - Beichel, Reinhard
A2 - Mori, Kensaku
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and Computer-Assisted Intervention Conference, MICCAI 2020
Y2 - 8 October 2020 through 8 October 2020
ER -