Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems-a New Paradigm.
J Digit Imaging
; 30(5): 629-639, 2017 Oct.
Article
em En
| MEDLINE
| ID: mdl-28405834
ABSTRACT
We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance. The pretrained algorithms were sequentially trained in the order of processing of the components. In this study, two different datasets, brain MRA with cerebral aneurysms and chest CT with lung nodules, were collected to develop two different types of CADe systems in the framework. The performances of the developed CADe systems were evaluated by threefold cross-validation. The CADe systems for detecting cerebral aneurysms in brain MRAs and for detecting lung nodules in chest CTs were successfully developed using the respective datasets. The framework was shown to be feasible by the successful development of the two different types of CADe systems. The feasibility of this framework shows promise for a new paradigm in the development of CADe systems development of CADe systems without any lesion specific algorithm designing.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Tomografia Computadorizada por Raios X
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Aneurisma Intracraniano
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Diagnóstico por Computador
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Angiografia por Ressonância Magnética
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Nódulos Pulmonares Múltiplos
Tipo de estudo:
Diagnostic_studies
Limite:
Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Digit Imaging
Assunto da revista:
DIAGNOSTICO POR IMAGEM
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INFORMATICA MEDICA
/
RADIOLOGIA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Japão