Dr. Akira Furukawa Profile

Dr. Akira Furukawa

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Publications (7)

Proceedings Article | 6 March 2008 Paper

A method of ROC analysis by applying item response theory (IRT) to results of 1/0 judgments on the presence or absence of abnormal findings in CT image readings

Toru Matsumoto, Akira Furukawa, Kanae Nisizawa, Kohei Murao, Kozo Hanai, Shinji Yamamoto, Shinichi Wada, Shusuke Sone

Proceedings Volume 6917, 69170Y (2008) https://doi.org/10.1117/12.771105

KEYWORDS: Computer aided design, Computed tomography, Computer aided diagnosis and therapy, Electronic filtering, Diagnostics, Computing systems, Data modeling, Target detection, Chest, Medical imaging

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Proceedings Article | 8 March 2007 Paper

A proposal of the evaluation method of the effectiveness of CAD use by means of the bias with variance characteristic (BVC) analysis and the internal structure analysis of the ROC-curve

Toru Matsumoto, Akira Furukawa, Kanae Nisizawa, Shinichi Wada, Shinji Yamamoto, Kohei Murao, Mitsuomi Matsumoto, Shusuke Sone, Kenjiro Fukuhisa, Takeshi Iinuma, Yukio Tateno

Proceedings Volume 6515, 651507 (2007) https://doi.org/10.1117/12.710450

KEYWORDS: Computer aided diagnosis and therapy, Computer aided design, Diagnostics, Electronic filtering, Alzheimer's disease, Single photon emission computed tomography, Computed tomography, Neuroimaging, Brain, Computing systems

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In this paper, we propose a methodology for evaluating whether the use of CAD is effective for any given reader or case, first analyzing the results of readers' judgments (0 or 1) by the technique known as analysis of bias-variance characteristics (BVC)^1,2, then by combining this with ROC analysis, elucidating the internal structure of the ROC curve. The mean and variance are first calculated for the situation when multiple readers examine a medical image for a single case without CAD and with CAD, and assign the values 0 and 1 to their judgment of whether abnormal findings are absent or present or whether the case is normal or abnormal. The mean of these values represents the degree of bias from the true diagnosis for the particular case, and the variance represents the spread of judgments between readers. When the relationship between the two parameters is examined for several cases with differing degrees of diagnostic difficulty, the mean (horizontal axis) and variance (vertical axis) show a bell-shaped relation. We have named this typical phenomenon arising when images are read, the bias-variance characteristic (BVC) of diagnosis. The mean of the 0 and 1 judgments of multiple readers is regarded as a measure of the confidence level determined for the particular case. ROC curves were drawn by usual methods for diagnoses made without CAD and with CAD. From the difference between the TPF obtained without CAD and with CAD for the same FPF on the ROC curve, we were able to quantify the number of cases, the total number of readers, and the total number of cases for which CAD support was beneficial. To demonstrate its usefulness, we applied this method to data obtained in a reading experiment that aimed to evaluate detection performance for abnormal findings and data obtained in a reading experiment that aimed to evaluate diagnostic discrimination performance for normal and abnormal cases. We analyzed the internal structure of the ROC curve produced when all cases were included, and showed that there is a relationship between the degree of diagnostic difficulty of the case and the benefit of CAD support and demonstrated that there are patients and readers for whom CAD is of benefit and those for whom it is not.

Proceedings Article | 17 March 2006 Paper

Explanation of the mechanism by which CAD assistance improves diagnostic performance when reading CT images

Toru Matsumoto, Shinichi Wada, Shinji Yamamoto, Kohei Murao, Akira Furukawa, Masahiro Endo, Mitsuomi Matsumoto, Shusuke Sone

Proceedings Volume 6146, 614619 (2006) https://doi.org/10.1117/12.652928

KEYWORDS: Computer aided diagnosis and therapy, Diagnostics, Computer aided design, Computed tomography, CAD systems, Databases, Lung cancer, Binary data, Medical imaging, Chest

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Proceedings Article | 17 March 2006 Paper

A proposal of the diagnosis-dynamic characteristic (DDC) model describing the relation between search time and confidence levels for a dichotomous judgment, and its application to ROC curve generation

Toru Matsumoto, Nobuo Fukuda, Akira Furukawa, Koji Suwa, Shinichi Wada, Mitsuomi Matsumoto, Shusuke Sone

Proceedings Volume 6146, 61460Y (2006) https://doi.org/10.1117/12.652933

KEYWORDS: Image quality, Inspection, Diagnostics, Time metrology, Medical imaging, Differential equations, Databases, Eye, Lung cancer, Computed tomography

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Proceedings Article | 6 April 2005 Paper

An assessment of the potential for interpretation of CT images by radiological technologists

Toru Matsumoto, Mitsuomi Matsumoto, Keiichi Nagao, Ryutaro Kakinuma, Shusuke Sone, Akira Furukawa, Yuichi Fujino, Shinichi Wada, Shinji Yamamoto, Kohei Murao, Masahiro Endo

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.596708

KEYWORDS: Computed tomography, CAD systems, Diagnostics, X-ray computed tomography, Lung cancer, Computer aided diagnosis and therapy, Databases, Computing systems, Computer aided design, Binary data

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Proceedings Article | 4 May 2004 Paper

Methods of evaluating the effectiveness of double-checking in interpreting mass screening images

Tohru Matsumoto, Akira Furukawa, Kikuo Machida, Norinari Honda, Tomoho Maeda, Mitsuomi Matsumoto, Yuichi Fujino, Shinichi Wada, Shusuke Sone, Kiminori Suzuki, Masahiro Endo

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.536304

KEYWORDS: CAD systems, Solid modeling, Computed tomography, Lung cancer, Computer aided diagnosis and therapy, Diagnostics, X-ray computed tomography, Cancer, X-rays, Systems modeling

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Proceedings Article | 22 May 2003 Paper

Relationship between changes in pupil size over time and diagnostic accuracy

Toru Matsumoto, Akira Furukawa, Megumu Tsuchikawa, Yuichi Fujino, Shusuke Sone

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480084

KEYWORDS: Computed tomography, CRTs, Eye, Diagnostics, Image processing, X-ray computed tomography, Lung cancer, Medical imaging, Motion analysis, Cancer

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Showing 5 of 7 publications

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