Preliminary study on the automated detection of breast tumors using the characteristic features from unenhanced MR images

Hayato Adachi; Atsushi Teramoto; Satomi Miyajo; Osamu Yamamuro; Kumiko Ohmi; Masami Nishio; Hiroshi Fujita

doi:10.1117/12.2081683

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20 March 2015 Preliminary study on the automated detection of breast tumors using the characteristic features from unenhanced MR images

Hayato Adachi, Atsushi Teramoto, Satomi Miyajo, Osamu Yamamuro, Kumiko Ohmi, Masami Nishio, Hiroshi Fujita

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Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94142A (2015) https://doi.org/10.1117/12.2081683
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Breast cancer incidence tends to rise globally and the mortality rate for breast cancer is increasing in Japan. There are various screening modalities for breast cancer, and MRI examinations with high detection rate are used for high-risk groups, which are genetically prone to develop breast cancer. In the breast MRI examination, unenhanced T1 and T2 weighted images shows no significant difference in signal value between tumor and normal tissue. Therefore, tumors are identified with use of contrast enhanced kinetic curve obtained by dynamic scan using contrast agent. Some computer aided diagnosis methods using dynamic contrast enhanced MR images also have been proposed. However, contrast agent produces the allergic reaction in rare case; it should not be used for screening examinees. Here, MRI provides the anatomical and functional information by using various sequences without contrast agents. According to the reports, this information can discriminate between tumor and normal tissue. In this study, we analyzed unenhanced MR images by using plural sequences and developed an automated method for the detection of tumors. First, we extracted the breast region from the T1-weighted image semi-automatically. Next, using the threshold determined by considering the signal intensities of tumor and normal tissue, a thresholding method was applied for diffusion-weighted image to extract the first candidate regions. After labeling processing, the breast region removes outside candidates from Initial candidates. Then false positives are reduced by the rule-based classifier. Finally, we examined the remaining candidates as possible tumor regions. We applied the proposed method to 54 cases of MR images and evaluated its usefulness. As a result, the detection sensitivity was 71.9% and the abnormal regions were clearly detected. These results indicate that the proposed method may be useful for tumor detection in unenhanced breast MR images.

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Hayato Adachi, Atsushi Teramoto, Satomi Miyajo, Osamu Yamamuro, Kumiko Ohmi, Masami Nishio, and Hiroshi Fujita "Preliminary study on the automated detection of breast tumors using the characteristic features from unenhanced MR images", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142A (20 March 2015); https://doi.org/10.1117/12.2081683

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