On the performance of lung nodule detection, segmentation and classification.
Comput Med Imaging Graph
; 89: 101886, 2021 04.
Article
en En
| MEDLINE
| ID: mdl-33706112
ABSTRACT
Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Tomografía Computarizada por Rayos X
/
Neoplasias Pulmonares
Tipo de estudio:
Diagnostic_studies
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Comput Med Imaging Graph
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2021
Tipo del documento:
Article
País de afiliación:
China