Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Emphysema detection and classification using a multi-scale deep convolutional neural network
Year:2017
Research Areas
  • Biomedicine,
  • Medical equipment,
  • Electronic technology and of the communications
Information
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide and leads to two main phenotypes: chronic bronchitis and emphysema. Densitometric analysis in CT is widely accepted measurement of emphysema, however it may not be able to classify it into subtypes. Others methods based on texture information have been proposed to carry out an emphysema classification. Texture patterns lead to six distinct types of emphysematous tissue: normal tissue (NT), paraseptal (PS), panlobular (PL) and mild, moderate and severe centrilobular (CL1, CL2, CL3) emphysema. In this article we propose and validate an emphysema pattern classification tool in CT images based on a Multi-scale Convolutional Neural Network (MCNN).
International
Si
Congress
31st Computer Assisted Radiology and Surgery (CARS 2017)
960
Place
Barcelona- España
Reviewers
Si
ISBN/ISSN
1861-6410
10.1007/s11548-017-1588-3
Start Date
20/06/2017
End Date
24/06/2017
From page
141
To page
143
Int. J. Comput. Assist. Radiol. Surg., vol. 12, no. suppl. 1,
Participants
  • Autor: Maria Jesus Ledesma Carbayo (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Tecnología de imágenes biomédicas
  • Centro o Instituto I+D+i: Centro de I+d+i en Procesado de la Información y Telecomunicaciones
  • Departamento: Ingeniería Electrónica
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