Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation.

Purpose: Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans. Approach: We introduce an automated DL-based approach that leverages anatomical information from the lung's vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net. Results: Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model's generalization capabilities. Finally, the method's robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases. Conclusions: Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.

Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography.

BACKGROUND: Computed tomography (CT) offers pulmonary volumetric quantification but is not commonly used in healthy individuals due to radiation concerns. Chronic airflow limitation (CAL) is one of the diagnostic criteria for chronic obstructive pulmonary disease (COPD), where early diagnosis is important. Our aim was to present reference values for chest CT volumetric and radiodensity measurements and explore their potential in detecting early signs of CAL. METHODS: From the population-based Swedish CArdioPulmonarybioImage Study (SCAPIS), 294 participants aged 50-64, were categorized into non-CAL (n = 258) and CAL (n = 36) groups based on spirometry. From inspiratory and expiratory CT images we compared lung volumes, mean lung density (MLD), percentage of low attenuation volume (LAV%) and LAV cluster volume between groups, and against reference values from static pulmonary function test (PFT). RESULTS: The CAL group exhibited larger lung volumes, higher LAV%, increased LAV cluster volume and lower MLD compared to the non-CAL group. Lung volumes significantly deviated from PFT values. Expiratory measurements yielded more reliable results for identifying CAL compared to inspiratory. Using a cut-off value of 0.6 for expiratory LAV%, we achieved sensitivity, specificity and positive/negative predictive values of 72%, 85% and 40%/96%, respectively. CONCLUSION: We present volumetric reference values from inspiratory and expiratory chest CT images for a middle-aged healthy cohort. These results are not directly comparable to those from PFTs. Measures of MLD and LAV can be valuable in the evaluation of suspected CAL. Further validation and refinement are necessary to demonstrate its potential as a decision support tool for early detection of COPD.

Flow Rate Deviation in Infusion Pump: Infusion Set Defect Enables Pump Malfunction and Considerable Accuracy Deviation.

Volumetric infusion pumps are used together with infusion sets to deliver medication to patients. Flow rate errors leading to overinfusion or underinfusion are known problems with these devices. Recently, numerous underinfusion flow rate errors were reported at a Swedish hospital. This experimental study reports on the investigation of these errors and specifically investigates the effect of operating the pump with a defective infusion set that has a visible elongation of the silicone segment of the set. Pump flow rate accuracy testing was performed using a gravimetric method. Experiments included a manipulated infusion set and a defective infusion set used in clinic. The use of a defective infusion set resulted in considerable accuracy deviations. The pump reported an infused amount greater than what was infused and did not provide any alarm or information indicating a reduced output. Using an elongated infusion set, the pump can be brought into an erroneous operating state where the infused amount delivered by the pump is considerably less than what has been programmed and what is shown on the pump display. This could put the patient at risk of not receiving the intended medication within the appropriate time.