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

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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.

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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.

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Authors and Affiliations

  1. Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Mitsutaka Nemoto, Naoto Hayashi, Yukihiro Nomura, Soichiro Miki & Takeharu Yoshikawa

  2. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Shouhei Hanaoka

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  1. Mitsutaka Nemoto
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  2. Naoto Hayashi
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  3. Shouhei Hanaoka
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  4. Yukihiro Nomura
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  5. Soichiro Miki
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  6. Takeharu Yoshikawa
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Corresponding author

Correspondence to Mitsutaka Nemoto.

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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.

Appendix: Calculation of the standard coordinate features

Appendix: Calculation of the standard coordinate features

The standard coordinate features were the standardized x, y, and z coordinates. To calculate the features, n a  atlas volumes were randomly chosen from the CADe training dataset and were registered to an input volume in advance. In this study, five atlas volumes and a volume registration method based on Markov random fields [29, 30] were used. The standard coordinate c p of voxel p in the input volume is given as follows:

$$ {\mathbf{c}}_p=\frac{1}{n_a}\sum_{i=1}^{n_a}{\mathbf{r}}_p^i.\kern1em $$
(9)

Here,\( {\mathbf{r}}_p^i \), which is the coordinate in the i th registered atlas volume, corresponds to p in the input volume.

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Nemoto, M., Hayashi, N., Hanaoka, S. et al. Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems—a New Paradigm. J Digit Imaging 30, 629–639 (2017). https://doi.org/10.1007/s10278-017-9968-3

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  • DOI: https://doi.org/10.1007/s10278-017-9968-3

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