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

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.

Impact of SARS-CoV-2 pandemic on pancreatic cancer services and treatment pathways: United Kingdom experience.

INTRODUCTION: The SARS-CoV-2 pandemic presented healthcare providers with an extreme challenge to provide cancer services. The impact upon the diagnostic and treatment capacity to treat pancreatic cancer is unclear. This study aimed to identify national variation in treatment pathways during the pandemic. METHODS: A survey was distributed to all United Kingdom pancreatic specialist centres, to assess diagnostic, therapeutic and interventional services availability, and alterations in treatment pathways. A repeating methodology enabled assessment over time as the pandemic evolved. RESULTS: Responses were received from all 29 centres. Over the first six weeks of the pandemic, less than a quarter of centres had normal availability of diagnostic pathways and a fifth of centres had no capacity whatsoever to undertake surgery. As the pandemic progressed services have gradually improved though most centres remain constrained to some degree. One third of centres changed their standard resectable pathway from surgery-first to neoadjuvant chemotherapy. Elderly patients, and those with COPD were less likely to be offered treatment during the pandemic. CONCLUSION: The COVID-19 pandemic has affected the capacity of the NHS to provide diagnostic and staging investigations for pancreatic cancer. The impact of revised treatment pathways has yet to be realised.