An open-source framework for pulmonary fissure completeness assessment.
Fissure completeness
Lung lobe fissures
Journal
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
ISSN: 1879-0771
Titre abrégé: Comput Med Imaging Graph
Pays: United States
ID NLM: 8806104
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
13
06
2019
revised:
02
12
2019
accepted:
17
02
2020
pubmed:
3
3
2020
medline:
14
9
2021
entrez:
3
3
2020
Statut:
ppublish
Résumé
We present an open-source framework for pulmonary fissure completeness assessment. Fissure incompleteness has been shown to associate with emphysema treatment outcomes, motivating the development of tools that facilitate completeness estimation. Generally, the task of fissure completeness assessment requires accurate detection of fissures and definition of the boundary surfaces separating the lung lobes. The framework we describe acknowledges a) the modular nature of fissure detection and lung lobe segmentation (lobe boundary detection), and b) that methods to address these challenges are varied and continually developing. It is designed to be readily deployable on existing lung lobe segmentation and fissure detection data sets. The framework consists of multiple components: a flexible quality control module that enables rapid assessment of lung lobe segmentations, an interactive lobe segmentation tool exposed through 3D Slicer for handling challenging cases, a flexible fissure representation using particles-based sampling that can handle fissure feature-strength or binary fissure detection images, and a module that performs fissure completeness estimation using voxel counting and a novel surface area estimation approach. We demonstrate the usage of the proposed framework by deploying on 100 cases exhibiting various levels of fissure completeness. We compare the two completeness level approaches and also compare to visual reads. The code is available to the community via github as part of the Chest Imaging Platform and a 3D Slicer extension module.
Identifiants
pubmed: 32115275
pii: S0895-6111(20)30015-X
doi: 10.1016/j.compmedimag.2020.101712
pmc: PMC7363554
mid: NIHMS1568189
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
101712Subventions
Organisme : NHLBI NIH HHS
ID : K25 HL130637
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL133137
Pays : United States
Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors do not hold any conflicts of interest relevant to this manuscript.
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