Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.Comment: 10 pages, 4 figure

Utility and reproducibility of 3-dimensional printed models in pre-operative planning of complex thoracic tumors

Background and objectives3D-printed models are increasingly used for surgical planning. We assessed the utility, accuracy, and reproducibility of 3D printing to assist visualization of complex thoracic tumors for surgical planning.MethodsModels were created from pre-operative images for three patients using a standard radiology 3D workstation. Operating surgeons assessed model utility using the Gillespie scale (1 = inferior to 4 = superior), and accuracy compared to intraoperative findings. Model variability was assessed for one patient for whom two models were created independently. The models were compared subjectively by surgeons and quantitatively based on overlap of depicted tissues, and differences in tumor volume and proximity to tissues.ResultsModels were superior to imaging and 3D visualization for surgical planning (mean score = 3.4), particularly for determining surgical approach (score = 4) and resectability (score = 3.7). Model accuracy was good to excellent. In the two models created for one patient, tissue volumes overlapped by >86.5%, and tumor volume and area of tissues ≤1 mm to the tumor differed by <15% and <1.8 cm2 , respectively. Surgeons considered these differences to have negligible effect on surgical planning.Conclusion3D printing assists surgical planning for complex thoracic tumors. Models can be created by radiologists using routine practice tools with sufficient accuracy and clinically negligible variability