RUO Home

Repositorio Institucional de la Universidad de Oviedo

View Item 
  •   RUO Home
  • Producción Bibliográfica de UniOvi: RECOPILA
  • Capítulos de libros
  • View Item
  •   RUO Home
  • Producción Bibliográfica de UniOvi: RECOPILA
  • Capítulos de libros
  • View Item
    • español
    • English
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

All of RUOCommunities and CollectionsBy Issue DateAuthorsTitlesSubjectsxmlui.ArtifactBrowser.Navigation.browse_issnAuthor profilesThis CollectionBy Issue DateAuthorsTitlesSubjectsxmlui.ArtifactBrowser.Navigation.browse_issn

My Account

LoginRegister

Statistics

View Usage Statistics

RECENTLY ADDED

Last submissions
Repository
How to publish
Resources
FAQs

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices In A Coarse-To-Fine Framework

Author:
Pang, S.; Du, A.; He, X.; Díez Peláez, JorgeUniovi authority; Orgun, M. A.
Subject:

Boundary delineation

Lung tumor segmentation

Fully convolutional neural networks

Publication date:
2019
Editorial:

Springer

Publisher version:
https://doi.org/10.1007/978-3-030-36808-1_64
Descripción física:
p. 589-597
Abstract:

Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to address the two challenges effectively and accurately in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, we propose an advanced transfer training and learning approach with a detailed DICOM pre-processing method. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to spot the Radiomics lung tumor regions accurately. Furthermore, an improved conditional random field method is employed to obtain sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approaches

Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to address the two challenges effectively and accurately in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, we propose an advanced transfer training and learning approach with a detailed DICOM pre-processing method. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to spot the Radiomics lung tumor regions accurately. Furthermore, an improved conditional random field method is employed to obtain sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approaches

URI:
http://hdl.handle.net/10651/53601
ISBN:
978-3-030-36807-4
DOI:
10.1007/978-3-030-36808-1_64
Collections
  • Capítulos de libros [4820]
  • Informática [409]
Files in this item
untranslated
Pang2019.pdf (2.548Mb)
Compartir
Exportar a Mendeley
Estadísticas de uso
Estadísticas de uso
Metadata
Show full item record
Página principal Uniovi

Biblioteca

Contacto

Facebook Universidad de OviedoTwitter Universidad de Oviedo
The content of the Repository, unless otherwise specified, is protected with a Creative Commons license: Attribution-Non Commercial-No Derivatives 4.0 Internacional
Creative Commons Image