Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN.

Autor: Connell M; Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA., Xin Y; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Gerard SE; Department of Radiology, Harvard Medical School, Boston, MA, USA., Herrmann J; Department of Biomedical Engineering, Boston University, Boston, MA, USA., Shah PK; Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Martin KT; Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Rezoagli E; Department of Emergency and Intensive Care, San Gerardo Hospital, Monza, Italy; Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy., Ippolito D; Department of Diagnostic and Interventional Radiology, San Gerardo Hospital, Monza, Italy., Rajaei J; Department of Medicine, Stanford University, Stanford, CA, USA., Baron R; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Delvecchio P; Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA., Humayun S; Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA., Rizi RR; Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA., Bellani G; Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy., Cereda M; Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: maurizio.cereda@uphs.upenn.edu.
Jazyk: angličtina
Zdroj: Methods (San Diego, Calif.) [Methods] 2022 Sep; Vol. 205, pp. 200-209. Date of Electronic Publication: 2022 Jul 08.
DOI: 10.1016/j.ymeth.2022.07.007
Abstrakt: Background: Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data.
Methods: We trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists.
Results: The COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations.
Conclusion: Our CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.
(Copyright © 2022. Published by Elsevier Inc.)
Databáze: MEDLINE
Externí odkaz: