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 / Pandey, Nidhi / 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 Internal medicine 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: 2024-10-12
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Diagnostics. 11 (2): 158-
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), 158-. DOI: 10.3390/diagnostics11020158
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2021
Tipo de publicación: Journal Publications
Repositori URV