A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images

Jiménez Carretero, Daniel and Bermejo Peláez, David and Nardelli, Pietro and Fraga Rivas, Patricia and Fraile Moreno, Eduardo and San José Estépar, Raúl and Ledesma Carbayo, Maria Jesus (2019). A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images. "Medical Image Analysis", v. 52 ; pp. 144-159. ISSN 1361-8415. https://doi.org/10.1016/j.media.2018.11.011.

Description

Title: A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images
Author/s:
  • Jiménez Carretero, Daniel
  • Bermejo Peláez, David
  • Nardelli, Pietro
  • Fraga Rivas, Patricia
  • Fraile Moreno, Eduardo
  • San José Estépar, Raúl
  • Ledesma Carbayo, Maria Jesus
Item Type: Article
Título de Revista/Publicación: Medical Image Analysis
Date: February 2019
ISSN: 1361-8415
Volume: 52
Subjects:
Freetext Keywords: Artery-vein segmentation; Lung; Graph-cuts; Random forest; Arteries; Veins; Noncontrast CT; Phantoms
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery–vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees. This framework has been applied to the pulmonary AV classification using a random forest (RF) pre-classifier to exploit the local anatomical differences of arteries and veins. The evaluation of the system was performed using 192 bronchopulmonary segment phantoms, 48 anthropomorphic pulmonary CT phantoms, and 26 lungs from noncontrast CT images with precise voxel-based reference standards obtained by manually labelling the vessel trees. The experiments reveal a relevant improvement in the accuracy ( ∼ 20%) of the vessel particle classification with the proposed framework with respect to using only the pre-classification based on local information applied to the whole area of the lung under study. The results demonstrated the accurate differentiation between arteries and veins in both clinical and synthetic cases, specifically when the image quality can guarantee a good airway segmentation, which opens a huge range of possibilities in the clinical study of cardiopulmonary diseases.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2013-48251-C2-2-RUnspecifiedUnspecifiedLanificación y guiado multimodal en cirugía y tratamiento de cáncer de mama

More information

Item ID: 64113
DC Identifier: https://oa.upm.es/64113/
OAI Identifier: oai:oa.upm.es:64113
DOI: 10.1016/j.media.2018.11.011
Official URL: https://www.sciencedirect.com/science/article/pii/S1361841518308740
Deposited by: Memoria Investigacion
Deposited on: 12 Dec 2020 09:26
Last Modified: 02 Mar 2021 23:30
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