Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 40, Issue 10, Pages 2808-2819Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2021.3066161
Keywords
Lesions; COVID-19; Computed tomography; Lung; Image segmentation; Training; Task analysis; COVID-19; label-free lesion segmentation; voxel-level anomaly modeling
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Funding
- CCF-Tencent Open Fund
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The study introduces a label-free approach for segmenting COVID-19 lesions in CT via voxel-level anomaly modeling, reducing the burden of data annotation. By learning patterns of normal tissues, a network capable of distinguishing normal tissues from COVID-19 lesions was established.
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via voxel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. Our modeling is inspired by the observation that the parts of tracheae and vessels, which lay in the high-intensity range where lesions belong to, exhibit strong patterns. To facilitate the learning of such patterns at a voxel level, we synthesize 'lesions' using a set of simple operations and insert the synthesized 'lesions' into normal CT lung scans to form training pairs, from which we learn a normalcy-recognizing network (NormNet) that recognizes normal tissues and separate them from possible COVID-19 lesions. Our experiments on three different public datasets validate the effectiveness of NormNet, which conspicuously outperforms a variety of unsupervised anomaly detection (UAD) methods.
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