English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Automated lobar segmentation allows regional evaluation of lung disease andis important for diagnosis and therapy planning. Advanced statistical workflowspermitting such evaluation is a needed area within respiratory medicine; theiradoption remains slow, with poor workflow accuracy. Diseased lung regions oftenproduce high-density zones on CT images, limiting an algorithm's execution tospecify damaged lobes due to oblique or lacking fissures. This impact motivateddeveloping an improved machine learning method to segment lung lobes thatutilises tracheobronchial tree information to enhance segmentation accuracythrough the algorithm's spatial familiarity to define lobar extent moreaccurately. The method undertakes parallel segmentation of lobes and auxiliarytissues simultaneously by employing multi-task learning (MTL) in conjunctionwith V-Net-attention, a popular convolutional neural network in the imagingrealm. In keeping with the model's adeptness for better generalisation, highperformance was retained in an external dataset of patients with four distinctdiseases: severe lung cancer, COVID-19 pneumonitis, collapsed lungs and ChronicObstructive Pulmonary Disease (COPD), even though the training data includednone of these cases. The benefit of our external validation test isspecifically relevant since our choice includes those patients who havediagnosed lung disease with associated radiological abnormalities. To ensureequal rank is given to all segmentations in the main task we report thefollowing performance (Dice score) on a per-segment basis: normal lungs 0.97,COPD 0.94, lung cancer 0.94, COVID-19 pneumonitis 0.94 and collapsed lung 0.92,all at p<0.05. Even segmenting lobes with large deformations on CT images, themodel maintained high accuracy. The approach can be readily adopted in theclinical setting as a robust tool for radiologists.

Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs