Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs.
Clin Radiol
; 77(8): e620-e627, 2022 08.
Article
in English
| MEDLINE | ID: covidwho-1867031
ABSTRACT
AIM:
To develop a multi-task learning (MTL) V-Net for pulmonary lobar segmentation on computed tomography (CT) and application to diseased lungs. MATERIALS ANDMETHODS:
The described methodology utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing MTL in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm. Its performance was validated by an external dataset of patients with four distinct lung conditions severe lung cancer, COVID-19 pneumonitis, collapsed lungs, and chronic obstructive pulmonary disease (COPD), even though the training data included none of these cases.RESULTS:
The following Dice scores were achieved 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.CONCLUSION:
Despite severe abnormalities, the model provided good performance at segmenting lobes, demonstrating the benefit of tissue learning. The proposed model is poised for adoption in the clinical setting as a robust tool for radiologists and researchers to define the lobar distribution of lung diseases and aid in disease treatment planning.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pulmonary Disease, Chronic Obstructive
/
COVID-19
/
Lung Neoplasms
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Clin Radiol
Year:
2022
Document Type:
Article
Affiliation country:
J.crad.2022.04.012
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