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Purpose: This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images.
Methods: Sixty thoracic CT scans provided by three different institutions were separated into 36 training, 12 offline testing, and 12 online testing scans. Eleven participants completed the offline challenge, and seven completed the online challenge. The OARs were left and right lungs, heart, esophagus, and spinal cord. Clinical contours used for treatment planning were quality checked and edited to adhere to the RTOG 1106 contouring guidelines. Algorithms were evaluated using the Dice coefficient, Hausdorff distance, and mean surface distance. A consolidated score was computed by normalizing the metrics against interrater variability and averaging over all patients and structures.
Results: The interrater study revealed highest variability in Dice for the esophagus and spinal cord, and in surface distances for lungs and heart. Five out of seven algorithms that participated in the online challenge employed deep-learning methods. Although the top three participants using deep learning produced the best segmentation for all structures, there was no significant difference in the performance among them. The fourth place participant used a multi-atlas-based approach. The highest Dice scores were produced for lungs, with averages ranging from 0.95 to 0.98, while the lowest Dice scores were produced for esophagus, with a range of 0.55-0.72.
Conclusion: The results of the challenge showed that the lungs and heart can be segmented fairly accurately by various algorithms, while deep-learning methods performed better on the esophagus. Our dataset together with the manual contours for all training cases continues to be available publicly as an ongoing benchmarking resource.
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http://dx.doi.org/10.1002/mp.13141 | DOI Listing |
Rheumatology (Oxford)
December 2024
Centre for Rheumatic Diseases, Department of Inflammation Biology, King's College London, London, UK.
Objectives: To update the first-line conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) prescribing pattern, describe change and variation across demographical and geographical factors in the Rheumatoid arthritis (RA) population, and identify individual and hospital factors associated with it.
Methods: This retrospective cohort study included newly diagnosed RA adult patients from 1 May 2018-1 April 2023 in the UK. We used adjusted multinomial logistic regression with random effect to explore associations with different first-line csDMRAD prescription and to account for hospital-level clustering.
Radiology
December 2024
From the Robarts Research Institute, Department of Medical Biophysics, Division of Respirology, Department of Medicine, and School of Biomedical Engineering, Western University, 1151 Richmond St N, Rm 5235, London, ON, Canada N6A 5B7 (G.P.); and Centre for Heart Lung Innovation, St. Paul's Hospital, BC Children's Hospital Research Institute, Department of Radiology, Department of Pediatrics, University of British Columbia, Vancouver, Canada (R.L.E.).
Ann Hum Genet
December 2024
Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Introduction: The common genetic underpinnings of psoriasis and pulmonary comorbidities have yet to be explored.
Material And Methods: In this cross-sectional study, we investigated the single-nucleotide polymorphisms (SNPs) associated with psoriasis and their relationship with pulmonary function using data from the UK Biobank (UKBB) and the Vanderbilt University Medical Center Biobank (BioVU).
Results: Out of the 63 psoriasis-associated SNPs identified in previous genome-wide association studies within the European population, we successfully identified 53 SNPs, including proxy SNPs in UKBB database.
J Appl Physiol (1985)
December 2024
Department of Kinesiology, Faculty of Medicine, Université Laval, Québec, Canada.
The brain is highly innervated by sympathetic nerve fibres; however, their physiological purpose is poorly understood. We hypothesized that unilateral cerebral norepinephrine (NE) spillover, an index of cerebral sympathetic nerve activity (SNA), would be elevated when engaging the baroreflex [via lower-body negative pressure (LBNP; -20 and -40 Torr)] and respiratory chemoreflexes [via carbon dioxide (CO) administration (+8 Torr)], independently, and in combination. Twelve young and healthy participants (5 females) underwent simultaneous blood sampling from the right radial artery and internal jugular vein.
View Article and Find Full Text PDFLancet Infect Dis
December 2024
Cincinnati Children's Hospital, Cincinnati, OH, USA.
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