Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.
Appl Opt
; 59(33): 10312-10320, 2020 Nov 20.
Article
en En
| MEDLINE
| ID: mdl-33361962
ABSTRACT
Disease classification and lesion segmentation of retinal optical coherence tomography images play important roles in ophthalmic computer-aided diagnosis. However, existing methods achieve the two tasks separately, which is insufficient for clinical application and ignores the internal relation of disease and lesion features. In this paper, a framework of cascaded convolutional networks is proposed to jointly classify retinal diseases and segment lesions. First, we adopt an auxiliary binary classification network to identify normal and abnormal images. Then a novel, to the best of our knowledge, U-shaped multi-task network, BDA-Net, combined with a bidirectional decoder and self-attention mechanism, is used to further analyze abnormal images. Experimental results show that the proposed method reaches an accuracy of 0.9913 in classification and achieves an improvement of around 3% in Dice compared to the baseline U-shaped model in segmentation.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Enfermedades de la Retina
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Procesamiento de Imagen Asistido por Computador
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Redes Neurales de la Computación
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Tomografía de Coherencia Óptica
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Appl Opt
Año:
2020
Tipo del documento:
Article