@techreport{oai:ipsj.ixsq.nii.ac.jp:00157922,
 author = {水野, 雄太 and 瀬尾, 茂人 and 渡邊, 誓旅 and 竹中, 要一 and 平松, 拓郎 and 後藤, 剛 and 河田, 照雄 and 松田, 秀雄 and Yuta, Mizuno and Shigeto, Seno and Seiryo, Watanabe and Yoichi, Takenaka and Takuro, Hiramatsu and Tsuyoshi, Goto and Teruo, Kawada and Hideo, Matsuda},
 issue = {10},
 month = {Mar},
 note = {内臓脂肪の過剰蓄積は，糖尿病や高脂血症，動脈硬化症などの生活習慣病発症の主要因となるため，肥満研究において脂肪組織の特徴を解析することは重要な課題である．肥満に関する脂肪組織の特徴として最も端的な事柄は，脂肪細胞の肥大化であり，そのため細胞の大きさと数を計測する必要がある．この計測には様々な方法が用いられるが，脂肪細胞切片を染色し撮影した画像から情報処理技術を用いて手動・自動で計数を行うのも一般的な方法である．しかしながら，手動で計測するには細胞数が多いため労力がかかり，また従来行われてきた自動的な処理では撮影環境の違いやノイズによる精度の低下が問題とされている．そこで本研究では，近年発展の目覚ましい機械学習の技法のひとつである Deep Convolutional Network に注目し，脂肪細胞画像から細胞領域の認識を行うために利用した．, It is important problem to analyze the characteristics of adipose tissue in an obese study, because the excessive accumulation of the internal organs lipid causes the lifestyle-related disease onset such as diabetes and hyperlipidemia, the arteriosclerosis. The most straightforward matter of adipose tissue about obesity is enlargement of the adipocytes, therefore it is necessary to measure size and quantity of adipocytes. Various methods are used for this measurement, and one of the typical method is to exploit information processing technology. However, manual methods take labor because of amount of cells, and automated processings which has been used conventionally is considered the drop of the accuracy, which caused by a differences of the photography environments, or noises included in the images. Therefore, in this study, we payed attention to Deep Convolutional Network which is one of the most remarkable technologies of the machine learning in late years, and used it for automated recognition of adipocytes areas in images of adipose tissue.},
 title = {Deep learningを用いた脂肪組織画像における細胞の認識},
 year = {2016}
}