Journal of Digital Imaging

, Volume 30, Issue 5, pp 629–639 | Cite as

Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems—a New Paradigm

  • Mitsutaka Nemoto
  • Naoto Hayashi
  • Shouhei Hanaoka
  • Yukihiro Nomura
  • Soichiro Miki
  • Takeharu Yoshikawa
Article
First Online:

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.

Keywords

Computer-aided detection (CADe) system Generalized CADe framework Automatic optimization Machine learning method CADe training dataset 
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Notes

Acknowledgements

The Department of Computational Radiology and Preventive Medicine, The University of Tokyo Hospital is sponsored by HIMEDIC Inc., Siemens Japan K.K., and GE Healthcare Japan.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Standards

The study described in this manuscript was approved by the Research Ethics Board of the University of Tokyo Hospital. Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  1. 1.Department of Computational Diagnostic Radiology and Preventive MedicineThe University of Tokyo HospitalTokyoJapan
  2. 2.Department of RadiologyThe University of Tokyo HospitalTokyoJapan

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