Lubrav: A New Framework For The Segmentation Of The Lung's Tubular Structures

The segmentation of the bronchus tree and pulmonary arteries and veins in CT scans plays an important role in patient care for both diagnosis and treatment phases. The extraction of theses tubular structures using either manual or interactive segmentation tools is time-consuming and prone to error d...

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Bibliographic Details
Published in2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) pp. 948 - 952
Main Authors Heitz, Adrien, Weinzorn, Julien, Noblet, Vincent, Naegel, Benoit, Charnoz, Arnaud, Heitz, Fabrice, Soler, Luc
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.04.2021
Subjects
2.5DU-Net
Computed tomography
FCN
Fusion
Image segmentation
Lung
Manuals
Neural networks
Pipelines
Thoracic segmentation
Tubular structure segmentation
Veins
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Summary:The segmentation of the bronchus tree and pulmonary arteries and veins in CT scans plays an important role in patient care for both diagnosis and treatment phases. The extraction of theses tubular structures using either manual or interactive segmentation tools is time-consuming and prone to error due to the complexity of aerial and vascular trees. In this work, we propose a fully automatic method, relying on cascaded convolutional neural networks, to tackle lungs, bronchus and pulmonary arteries and veins segmentation. The first component based on a 2DU-Net architecture is dedicated to right and left lung segmentation. The second component relies on a three-paths 2.5D fully convolutional networks along axial, coronal and sagittal slices and focuses on tubular structures. Performance is assessed on a database of 193 chest CT scans.
ISSN:1945-8452
DOI:10.1109/ISBI48211.2021.9434041