Creating a training set for artificial intelligence from initial segmentations of airways

Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2-4 h per scan. Manually corrected segmentations had more branches (p < 0.001), longer airways (p < 0.001) and smaller luminal diameters (p = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset

Bronchial wall parameters on CT in healthy never-smoking, smoking, COPD, and asthma populations:a systematic review and meta-analysis

OBJECTIVE: Research on computed tomography (CT) bronchial parameter measurements shows that there are conflicting results on the values for bronchial parameters in the never-smoking, smoking, asthma, and chronic obstructive pulmonary disease (COPD) populations. This review assesses the current CT methods for obtaining bronchial wall parameters and their comparison between populations. METHODS: A systematic review of MEDLINE and Embase was conducted following PRISMA guidelines (last search date 25th October 2021). Methodology data was collected and summarised. Values of percentage wall area (WA%), wall thickness (WT), summary airway measure (Pi10), and luminal area (Ai) were pooled and compared between populations. RESULTS: A total of 169 articles were included for methodologic review; 66 of these were included for meta-analysis. Most measurements were obtained from multiplanar reconstructions of segmented airways (93 of 169 articles), using various tools and algorithms; third generation airways in the upper and lower lobes were most frequently studied. COPD (12,746) and smoking (15,092) populations were largest across studies and mostly consisted of men (median 64.4%, IQR 61.5 - 66.1%). There were significant differences between populations; the largest WA% was found in COPD (mean SD 62.93 ± 7.41%, n = 6,045), and the asthma population had the largest Pi10 (4.03 ± 0.27 mm, n = 442). Ai normalised to body surface area (Ai/BSA) (12.46 ± 4 mm2, n = 134) was largest in the never-smoking population. CONCLUSIONS: Studies on CT-derived bronchial parameter measurements are heterogenous in methodology and population, resulting in challenges to compare outcomes between studies. Significant differences between populations exist for several parameters, most notably in the wall area percentage; however, there is a large overlap in their ranges. KEY POINTS: • Diverse methodology in measuring airways contributes to overlap in ranges of bronchial parameters among the never-smoking, smoking, COPD, and asthma populations. • The combined number of never-smoking participants in studies is low, limiting insight into this population and the impact of participant characteristics on bronchial parameters. • Wall area percent of the right upper lobe apical segment is the most studied (87 articles) and differentiates all except smoking vs asthma populations

Improved precision of noise estimation in CT with a volume-based approach

Assessment of image noise is a relevant issue in computed tomography (CT). Noise is routinely measured by the standard deviation of density values (Hounsfield units, HU) within a circular region of interest (ROI). We explored the effect of a spherical volume of interest (VOI) on noise measurements. Forty-nine chronic obstructive pulmonary disease patients underwent CT with clinical protocol (regular dose [RD], volumetric CT dose index [CTDIvol] 3.04 mGy, 64-slice unit), and ultra-low dose (ULD) protocol (median CTDIvol 0.38 mGy, dual-source unit). Noise was measured in 27 1-cm(2) ROIs and 27 0.75-cm(3) VOIs inside the trachea. Median true noise was 21 HU (range 17-29) for RD-CT and 33 HU (26-39) for ULD-CT. The VOI approach resulted in a lower mean distance between limits of agreement compared to ROI: 5.9 versus 10.0 HU for RD-CT (−40%); 4.7 versus 9.9 HU for ULD-CT (−53%). Mean systematic bias barely changed: −1.6 versus −0.9HU for RD-CT; 0.0 to 0.4HU for ULD-CT. The average measurement time was 6.8 s (ROI) versus 9.7 (VOI), independent of dose level. For chest CT, measuring noise with a VOI-based instead of a ROI-based approach reduces variability by 40-53%, without a relevant effect on systematic bias and measurement time

Communicative Nature of the Productive Imagination

У статті наведено думку, що продуктивна уява постає не просто засобом комунікації, але сама має виразну комунікативну природу. Продуктивна уява є здатністю бачити світ очима роду людського, очима інших людей, реально в них не перевтілюючись. Продуктивна уява у статті розглядається у контексті дійсної породжувальної основи людського буття – суцільних звернень людської суб’єктивності до суб’єктивності інших у пошуках спорідненості, злагоди, спів-чуття, спів-розуміння.The article represents the idea that productive imagination appears not only as a means of communication, but also has a clear communicative nature. Usually the imagination is interpreted as the activity of combining famous images, their elements, resulting in something «fictitious», something «new», «that never happened before». Imagination (fantasy) seems to tear away a person from the surrounding reality. However, productive imagination, on the contrary, gives a unique opportunity to see what is real. In order to perceive an object, to see it, the object must be reduced to an image, imagine, and only in such an image, it becomes visible as it is in its essence. An animal perceives the world through the eyes of its species and this ability is transmitted genetically. Human’s productive imagination is the ability to see the world through the eyes of the human race, through the eyes of other people, not really transforming them into oneself. This ability captures the individual in the process of mastering the forms of human life. From the outset, from the cradle, «the experience» of the child is formed solely by appropriating the experience of others, adults, the experience of generations of the human race. Only in this way is formed «her own point of view», «his experience». Productive imagination appears not only as one of the abilities of man, it acts as the ability to absorb any ability, the basis of creativity of all forces of the soul, the driving force of the human soul

Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction

Objectives: Computed tomography (CT)–based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters.Methods: A deep-learning airway segmentation model was newly trained on 24 Imaging in Lifelines (ImaLife) low-dose chest CT scans. This model was combined with an optimal-surface graph-cut for airway wall segmentation. These tools were used to calculate bronchial parameters in CT scans of 188 ImaLife participants with two scans an average of 3 months apart. Bronchial parameters were compared for reproducibility assessment, assuming no change between scans.Results: Of 376 CT scans, 374 (99%) were successfully measured. Segmented airway trees contained a mean of 10 generations and 250 branches. The coefficient of determination (R2) for the luminal area (LA) ranged from 0.93 at the trachea to 0.68 at the 6th generation, decreasing to 0.51 at the 8th generation. Corresponding values for Wall Area Percentage (WAP) were 0.86, 0.67, and 0.42, respectively. Bland–Altman analysis of LA and WAP per generation demonstrated mean differences close to 0; limits of agreement (LoA) were narrow for WAP and Pi10 (± 3.7% of mean) and wider for LA (± 16.4–22.8% for 2–6th generations). From the 7th generation onwards, there was a sharp decrease in reproducibility and a widening LoA.Conclusion: The outlined approach for automatic bronchial parameter measurement on low-dose chest CT scans is a reliable way to assess the airway tree down to the 6th generation. Statement on clinical relevance: This reliable and fully automatic pipeline for bronchial parameter measurement on low-dose CT scans has potential applications in screening for early disease and clinical tasks such as virtual bronchoscopy or surgical planning, while also enabling the exploration of bronchial parameters in large datasets. Key Points: • Deep learning combined with optimal-surface graph-cut provides accurate airway lumen and wall segmentations on low-dose CT scans. • Analysis of repeat scans showed that the automated tools had moderate-to-good reproducibility of bronchial measurements down to the 6thgeneration airway. • Automated measurement of bronchial parameters enables the assessment of large datasets with less man-hours.</p

Currents of Changes

The Power Systems in Denmark, Portugal, Spain, Ireland, and Germany have some of the highest wind penetrations in the world, as shown in Table 1. The management of the different power systems to date,with increasing amounts of wind energy, has been successful.There have been no reported incidents in which wind has directly or indirectly been a major factor causing operational problems on the system. In some areas with high wind penetration, however, the transmission system operator (TSO) had to increase remedial actions signifi cantly in order to decrease the loading of system assets during times of high wind power infeed. In some areas, the risk of faults may have increased. Higher targets for wind power will mean even higher penetration levels locally and high penetration levels in larger power systems. There are a number of issues that will require active management in the near future; in some cases, such management is needed today. In this article, the situations of five countries with high wind penetration are briefl y presented, with special emphasis given to their future needs with respect to accommodating targeted wind power amounts. The final section provides an overview of offshore grid developments and plans in Europe

Low-Dose CT–derived Bronchial Parameters in Individuals with Healthy Lungs

Background: CT-derived bronchial parameters have been linked to chronic obstructive pulmonary disease and asthma severity, but little is known about these parameters in healthy individuals.Purpose: To investigate the distribution of bronchial parameters at low-dose CT in individuals with healthy lungs from a Dutch general population. Materials and Methods: In this prospective study, low-dose chest CT performed between May 2017 and October 2022 were obtained from participants who had completed the second-round assessment of the prospective, longitudinal Imaging in Lifelines study. Participants were aged at least 45 years, and those with abnormal spirometry, self-reported respiratory disease, or signs of lung disease at CT were excluded. Airway lumens and walls were segmented automatically. The square root of the bronchial wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10), luminal area (LA), wall thickness (WT), and wall area percentage were calculated. Associations between sex, age, height, weight, smoking status, and bronchial parameters were assessed using univariable and multivariable analyses. Results: The study sample was composed of 8869 participants with healthy lungs (mean age, 60.9 years ± 10.4 [SD]; 4841 [54.6%] female participants), including 3672 (41.4%) never-smokers and 1197 (13.5%) individuals who currently smoke. Bronchial parameters for male participants were higher than those for female participants (Pi10, slope [β] range = 3.49–3.66 mm; LA, β range = 25.40–29.76 mm2; WT, β range = 0.98–1.03 mm; all P &lt; .001). Increasing age correlated with higher Pi10, LA, and WT (r2 range = 0.06–0.09, 0.02–0.01, and 0.02–0.07, respectively; all P &lt; .001). Never-smoking individuals had the lowest Pi10 followed by formerly smoking and currently smoking individuals (3.62 mm ± 0.13, 3.68 mm ± 0.14, and 3.70 mm ± 0.14, respectively; all P &lt; .001). In multivariable regression models, age, sex, height, weight, and smoking history explained up to 46% of the variation in bronchial parameters. Conclusion:In healthy individuals, bronchial parameters differed by sex, height, weight, and smoking history; male sex and increasing age were associated with wider lumens and thicker walls.</p