Investigation of the effect of image resolution on automatic classification of mammary gland density in mammography images using deep learning

Ami Oshima; Norimitsu Shinohara; Naoki Kamiya

doi:10.1117/12.2521255

27 March 2019 Investigation of the effect of image resolution on automatic classification of mammary gland density in mammography images using deep learning

Ami Oshima, Norimitsu Shinohara, Naoki Kamiya

Author Affiliations +

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 1105018 (2019) https://doi.org/10.1117/12.2521255
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

Mammary gland density is used as one of the measures in managing the risk of breast cancer. It can be divided into four categories. In addition, mammography is used for population-based breast cancer screening in Japan. However, mass and calcification are assumed to be hidden in the shadow of the mammary gland as displayed by the mammogram when patients showing heterogeneously dense or extremely dense in the mammary gland density category are scanned with mammography. Therefore, it is necessary to recommend an examination suitable for each category of mammary gland density. In one example, a doctor recommends ultrasonography in addition to mammography for patients with dense breasts. However, mammary gland density is distinguished visually using subjective judgment. Against such a background, we have worked on an automatic classification of mammary gland densities using a deep learning technique. Moreover, we investigated the effect of image resolution on the classification results in the automatic classification of mammary gland density with deep learning. The resolution was varied from 1/100 (474 × 354) to 1/3600 (79 × 59) using 1106 cases of resolution 4740 × 3540 (pixels) obtained with Fuji Computed Radiography (FCR) by Fujifilm Co. Ltd. As a result, the accuracy of automatic classification of mammary gland density exceeded 90% up to a resolution of 1/400 (237 × 177), and was 89% even at the lowest resolution of 1/3600 (79 × 59).

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Ami Oshima, Norimitsu Shinohara, and Naoki Kamiya "Investigation of the effect of image resolution on automatic classification of mammary gland density in mammography images using deep learning", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105018 (27 March 2019); https://doi.org/10.1117/12.2521255

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