Tomohiro Hirose

Publications (2)3.48 Total impact

• Article: Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.

  Tomohiro Hirose, Norihisa Nitta, Junji Shiraishi, Yukihiro Nagatani, Masashi Takahashi, Kiyoshi Murata
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  ABSTRACT: The aim of this study was to evaluate the usefulness of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector-row computed tomography (MDCT) in terms of improvement in radiologists' diagnostic accuracy in detecting lung nodules, using jackknife free-response receiver-operating characteristic (JAFROC) analysis. Twenty-one patients (6 without and 15 with lung nodules) were selected randomly from 120 consecutive thoracic computed tomographic examinations. The gold standard for the presence or absence of nodules in the observer study was determined by consensus of two radiologists. Six expert radiologists participated in a free-response receiver operating characteristic study for the detection of lung nodules on MDCT, in which cases were interpreted first without and then with the output of CAD software. Radiologists were asked to indicate the locations of lung nodule candidates on the monitor with their confidence ratings for the presence of lung nodules. The performance of the CAD software indicated that the sensitivity in detecting lung nodules was 71.4%, with 0.95 false-positive results per case. When radiologists used the CAD software, the average sensitivity improved from 39.5% to 81.0%, with an increase in the average number of false-positive results from 0.14 to 0.89 per case. The average figure-of-merit values for the six radiologists were 0.390 without and 0.845 with the output of the CAD software, and there was a statistically significant difference (P < .0001) using the JAFROC analysis. The CAD software for the detection of lung nodules on MDCT has the potential to assist radiologists by increasing their accuracy.
  Academic radiology 12/2008; 15(12):1505-12. · 2.09 Impact Factor
• Article: Automatic liver segmentation method featuring a novel filter for multiphase multidetector-row helical computed tomography.

  Tomohiro Hirose, Norihisa Nitta, Masaru Tsudagawa, Masashi Takahashi, Kiyoshi Murata
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  ABSTRACT: To introduce an automatic liver segmentation method that includes a novel filter for multiphase multidetector-row helical computed tomography. We acquired 3-phase multidetector-row computed tomographic scans that included unenhanced, arterial, and portal phases. The liver was segmented using our novel adaptive linear prediction filter designed to reduce the difference between filter input and output values in the liver region and to increase these values outside the liver region. The segmentation algorithm produced a mean dice similarity coefficient (DSC) value of 91.4%. The application of our adaptive linear prediction filter was effective in automatically extracting liver regions.
  Journal of computer assisted tomography 35(3):347-50. · 1.38 Impact Factor