Abstract
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).
T. Li and Z. Wang—Contributed equally.
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Li, T. et al. (2020). Two-Stage Mapping-Segmentation Framework for Delineating COVID-19 Infections from Heterogeneous CT Images. In: Petersen, J., et al. Thoracic Image Analysis. TIA 2020. Lecture Notes in Computer Science(), vol 12502. Springer, Cham. https://doi.org/10.1007/978-3-030-62469-9_1
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DOI: https://doi.org/10.1007/978-3-030-62469-9_1
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