Articles producció científica> Enginyeria Informàtica i Matemàtiques

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

  • Datos identificativos

    Identificador: imarina:9173256
    Handle: http://hdl.handle.net/20.500.11797/imarina9173256
  • Autores:

    Singh, Vivek Kumar
    Abdel-Nasser, Mohamed
    Pandey, Nidhi
    Puig, Domenec
  • Otros:

    Autor según el artículo: Singh, Vivek Kumar; Abdel-Nasser, Mohamed; Pandey, Nidhi; Puig, Domenec;
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Palabras clave: X-ray computed tomography Receptive field Image segmentation Human Feature extraction Feature attention module False positive result Discrete wavelet transform Deep learning Decomposition Ct slices Covid-19 Coronavirus disease 2019 Controlled study Comparative study Article
    Resumen: COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34% and an intersection-over-union (IoU) score of 68.77%-higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10% better than those of the popular biomedical segmentation method U-Net.
    Áreas temáticas: Medicine, general & internal Clinical biochemistry
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
    Identificador del autor: 0000-0002-1074-2441 0000-0002-0562-4205
    Fecha de alta del registro: 2023-05-13
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2075-4418/11/2/158
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Diagnostics. 11 (2):
    Referencia de l'ítem segons les normes APA: Singh, Vivek Kumar; Abdel-Nasser, Mohamed; Pandey, Nidhi; Puig, Domenec; (2021). LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework. Diagnostics, 11(2), -. DOI: 10.3390/diagnostics11020158
    DOI del artículo: 10.3390/diagnostics11020158
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2021
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Clinical Biochemistry,Medicine, General & Internal
    X-ray computed tomography
    Receptive field
    Image segmentation
    Human
    Feature extraction
    Feature attention module
    False positive result
    Discrete wavelet transform
    Deep learning
    Decomposition
    Ct slices
    Covid-19
    Coronavirus disease 2019
    Controlled study
    Comparative study
    Article
    Medicine, general & internal
    Clinical biochemistry
  • Documentos:

  • Cerca a google

    Search to google scholar