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).
The skeletal muscle exists in the whole body and can be observed in many cross sections in various tomographic images. Skeletal muscle atrophy is due to aging and disease, and the abnormality is difficult to distinguish visually. In addition, although skeletal muscle analysis requires a technique for accurate site-specific measurement of skeletal muscle, it is only realized in a limited region. We realized automatic site-specific recognition of skeletal muscle from whole-body CT images using model-based methods. Three-dimensional texture analysis revealed imaging features with statistically significant differences between amyotrophic lateral sclerosis (ALS) and other muscular diseases accompanied by atrophy. In recent years, deep learning technique is also used in the field of computer-aided diagnosis. Therefore, in this initial study, we performed automatic classification of amyotrophic diseases using deep learning for the upper extremity and lower limb regions. The classification accuracy was highest in the right forearm, which was 0.960 at the maximum (0.903 on average). In the future, methods for differentiating more kinds of muscular atrophy and clinical application of ALS detection by analyzing muscular regions must be considered.
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