Perspectives on lung visualization: Three-dimensional anatomical modeling of computed and micro-computed tomographic data in comparative evolutionary morphology and medicine with applications for COVID-19.
Emma R SchachnerAdam B LawsonAracely MartinezClinton A Grand PreCarl SabottkeFarid Abou-IssaScott EcholsRaul E DiazAndrew J MooreJohn-Paul GrenierBrandon P HedrickBradley M SpielerPublished in: Anatomical record (Hoboken, N.J. : 2007) (2023)
The vertebrate respiratory system is challenging to study. The complex relationship between the lungs and adjacent tissues, the vast structural diversity of the respiratory system both within individuals and between taxa, its mobility (or immobility) and distensibility, and the difficulty of quantifying and visualizing functionally important internal negative spaces have all impeded descriptive, functional, and comparative research. As a result, there is a relative paucity of three-dimensional anatomical information on this organ system in all vertebrate groups (including humans) relative to other regions of the body. We present some of the challenges associated with evaluating and visualizing the vertebrate respiratory system using computed and micro-computed tomography and its subsequent digital segmentation. We discuss common mistakes to avoid when imaging deceased and live specimens and various methods for merging manual and threshold-based segmentation approaches to visualize pulmonary tissues across a broad range of vertebrate taxa, with a particular focus on sauropsids (reptiles and birds). We also address some of the recent work in comparative evolutionary morphology and medicine that have used these techniques to visualize respiratory tissues. Finally, we provide a clinical study on COVID-19 in humans in which we apply modeling methods to visualize and quantify pulmonary infection in the lungs of human patients.
Keyphrases
- coronavirus disease
- computed tomography
- sars cov
- gene expression
- end stage renal disease
- pulmonary hypertension
- deep learning
- endothelial cells
- convolutional neural network
- newly diagnosed
- chronic kidney disease
- ejection fraction
- prognostic factors
- magnetic resonance imaging
- high resolution
- peritoneal dialysis
- healthcare
- positron emission tomography
- magnetic resonance
- diffusion weighted imaging
- cross sectional
- electronic health record
- health information
- machine learning
- clinical trial
- kidney transplantation
- patient reported outcomes
- contrast enhanced
- single molecule