Prostate cancer detection using residual networks.
Int J Comput Assist Radiol Surg
; 14(10): 1647-1650, 2019 Oct.
Article
em En
| MEDLINE
| ID: mdl-30972686
ABSTRACT
PURPOSE:
To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI).METHODS:
A residual network was implemented based on segmentations from an expert radiologist on T2-weighted, apparent diffusion coefficient map, and high b-value diffusion-weighted images. Mp-MRIs from 346 patients were used in this study.RESULTS:
The residual network achieved a hit or miss accuracy of 93% for lesion detection, with an average Jaccard score of 71% that compared the agreement between network and radiologist segmentations.CONCLUSION:
This paper demonstrated the ability for residual networks to learn features for prostate lesion segmentation.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Próstata
/
Diagnóstico por Computador
/
Redes Neurais de Computação
/
Imagem de Difusão por Ressonância Magnética
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
/
Male
Idioma:
En
Revista:
Int J Comput Assist Radiol Surg
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Canadá