Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

13-03-2020 | Original Article | Issue 10/2020

International Journal of Machine Learning and Cybernetics 10/2020

PRF-RW: a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation

Journal:
International Journal of Machine Learning and Cybernetics > Issue 10/2020
Authors:
Qiang Li, Lei Chen, Xiangju Li, Xiaofeng Lv, Shuyue Xia, Yan Kang
Important notes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

The computational detection of lung lobes from computed tomography images is a challenging segmentation problem with important respiratory healthcare applications, including emphysema, chronic bronchitis, and asthma. This paper proposes a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation. First, our model performs automated segmentation of the lung lobes in a progressive random forest network, eliminating the need for prior segmentation of lungs, vessels, or airways. Then, an interactive lobes segmentation approach based on random walk mechanism is designed for improving auto-segmentation accuracy. Furthermore, we annotate a new dataset which contains 93 scans (57 men, 36 women; age range: 40–90 years) from the Central Hospital Affiliated with Shenyang Medical College (CHASMC). We evaluate the model on our annotated dataset, LIDC (https://​wiki.​cancerimagingarc​hive.​net) and LOLA11 (http://​lolall.​com/​) datasets. The proposed model achieved a Dice score of \(0.906 \pm 0.106\) for LIDC, \(0.898 \pm 0.113\) for LOLA11, and \(0.921 \pm 0.101\) for our dataset. Experimental results show the accuracy of the proposed approach, which consistently improves performance across different datasets by a maximum of 8.2% as compared to baselines model.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 10/2020

International Journal of Machine Learning and Cybernetics 10/2020 Go to the issue