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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMedical Imaging 2015
Subtitle of host publicationComputer-Aided Diagnosis
EditorsLubomir M. Hadjiiski, Lubomir M. Hadjiiski, Georgia D. Tourassi, Georgia D. Tourassi
PublisherSPIE
Volume9414
ISBN (Electronic)9781628415049, 9781628415049
DOIs
Publication statusPublished - 01-01-2015
EventSPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis - Orlando, United States
Duration: 22-02-201525-02-2015

Other

OtherSPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis
CountryUnited States
CityOrlando
Period22-02-1525-02-15

Fingerprint

breast
Tumors
tumors
Breast Neoplasms
Breast
Neoplasms
Magnetic resonance imaging
Contrast Media
cancer
Tissue
Screening
screening
examination
Computer aided diagnosis
mortality
Labeling
classifiers
Japan
Hypersensitivity
Classifiers

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Adachi, H., Teramoto, A., Miyajo, S., Yamamuro, O., Ohmi, K., Nishio, M., & Fujita, H. (2015). Preliminary study on the automated detection of breast tumors using the characteristic features from unenhanced MR images. In L. M. Hadjiiski, L. M. Hadjiiski, G. D. Tourassi, & G. D. Tourassi (Eds.), Medical Imaging 2015: Computer-Aided Diagnosis (Vol. 9414). [94142A] SPIE. https://doi.org/10.1117/12.2081683
Adachi, Hayato ; Teramoto, Atsushi ; Miyajo, Satomi ; Yamamuro, Osamu ; Ohmi, Kumiko ; Nishio, Masami ; Fujita, Hiroshi. / Preliminary study on the automated detection of breast tumors using the characteristic features from unenhanced MR images. Medical Imaging 2015: Computer-Aided Diagnosis. editor / Lubomir M. Hadjiiski ; Lubomir M. Hadjiiski ; Georgia D. Tourassi ; Georgia D. Tourassi. Vol. 9414 SPIE, 2015.
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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.",
author = "Hayato Adachi and Atsushi Teramoto and Satomi Miyajo and Osamu Yamamuro and Kumiko Ohmi and Masami Nishio and Hiroshi Fujita",
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Adachi, H, Teramoto, A, Miyajo, S, Yamamuro, O, Ohmi, K, Nishio, M & Fujita, H 2015, Preliminary study on the automated detection of breast tumors using the characteristic features from unenhanced MR images. in LM Hadjiiski, LM Hadjiiski, GD Tourassi & GD Tourassi (eds), Medical Imaging 2015: Computer-Aided Diagnosis. vol. 9414, 94142A, SPIE, SPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis, Orlando, United States, 22-02-15. https://doi.org/10.1117/12.2081683

Preliminary study on the automated detection of breast tumors using the characteristic features from unenhanced MR images. / Adachi, Hayato; Teramoto, Atsushi; Miyajo, Satomi; Yamamuro, Osamu; Ohmi, Kumiko; Nishio, Masami; Fujita, Hiroshi.

Medical Imaging 2015: Computer-Aided Diagnosis. ed. / Lubomir M. Hadjiiski; Lubomir M. Hadjiiski; Georgia D. Tourassi; Georgia D. Tourassi. Vol. 9414 SPIE, 2015. 94142A.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Adachi, Hayato

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AU - Miyajo, Satomi

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AU - Ohmi, Kumiko

AU - Nishio, Masami

AU - Fujita, Hiroshi

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N2 - 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.

AB - 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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Adachi H, Teramoto A, Miyajo S, Yamamuro O, Ohmi K, Nishio M et al. Preliminary study on the automated detection of breast tumors using the characteristic features from unenhanced MR images. In Hadjiiski LM, Hadjiiski LM, Tourassi GD, Tourassi GD, editors, Medical Imaging 2015: Computer-Aided Diagnosis. Vol. 9414. SPIE. 2015. 94142A https://doi.org/10.1117/12.2081683