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.

Video Visits as a Substitute for Urological Clinic Visits.

OBJECTIVE: To evaluate whether video visits were being used as substitutes to clinic visits prior to COVID-19 at our institution's outpatient urology centers. METHODS: We reviewed 600 established patient video visits completed by 13 urology providers at a tertiary academic center in southeast Michigan. We compared these visits to a random, stratified sample of established patient clinic visits. We assessed baseline demographics and visit characteristics for both groups. We defined our primary outcome ("revisit rate") as the proportion of additional healthcare evaluation (ie, office, emergency room, hospitalization) by a urology provider within 30 days of the initial encounter. RESULTS: Patients seen by video visit tended to be younger (51 vs 61 years, P <.001), would have to travel further for a clinic appointment (82 vs 68 miles, P <.001), and were more likely to be female (36 vs 28%, P = .001). The most common diagnostic groups evaluated through video visits were nephrolithiasis (40%), oncology (18%) and andrology (14.3%). While the 30-day revisit rates were higher for clinic visits (4.3% vs 7.5%, P = .01) primarily due to previously scheduled appointments, revisits due to medical concerns were similar across both groups (0.5% vs 0.67%; P = .60). CONCLUSIONS: Video visits can be used to deliver care across a broad range of urologic diagnoses and can serve as a substitute for clinic visits.