Misc

＜文献概要＞藤浪剛一(1880〜1942)は,日本初の放射線科医といわれており,数々の放射線関連学会を創設するなど,日本の放射線医学の発展に大いに貢献した.慶應義塾大学医学部放射線科学教室は,日本初の放射線医学教室として藤浪により開設され,昨年2020年に創立100周年を迎えた.この機会に,過去の文献を参照しながら,X線の発見から放射線医学教室開設までの経緯,日本医学放射線学会の成り立ちなど,放射線医学の草創期を概説してみたい.

＜文献概要＞はじめに　運動器疾患の診断には単純X線撮影が,依然gold standardとして用いられている.とりわけ整形外科において取り扱う運動器疾患においては,臥位ではなく立位での撮影により,病態や診断がより明確となるものが多い.これは立位または荷重位にすることで,脊椎・関節がより生理的な位置となり,症状・病態を反映するためである.そのため立位CT検査が可能となれば,有用性は極めて高く,従来のX線に取り替わる画期的な診断装置となり得る.このような背景をもとに,われわれは2013年より東芝メディカル(現・キヤノンメディカルシステムズ)と全身用立位CTの開発に着手した.既存の320列CT(Aquilion ONE)を改良することで動態撮影も可能なプロトタイプを開発し,2017年5月より慶應義塾大学病院に導入した.本稿では全身用立位CTの紹介,および関節機能評価の例として下肢アライメント評価について述べる.

In medical imaging, landmarks have significant clinical and scientific importance. Clinical measurements, derived from the landmarks, are used for diagnosis. However, placing landmarks manually is a tedious task. In ultrasound imaging, measurement marker is generally used to mark a pair of landmarks by the operator to measure the geometry of the region of interest. Manual placement of the measurement markers is subject to inter-observer variability and there is a need to make the process automatic. The aim of this study is to develop a new landmark detection method that the domain is not limited using machine learning technique. The neural network model was designed and trained using about 50,000 images of various domains so that if one of the measurement markers is given as input, the second measurement marker position will be predicted. We evaluated the prediction accuracy and to confirm its usefulness.

Esophageal cancer has a 10-year survival rate of about 20%, which is a cancer with a high mortality rate along with pancreatic cancer. It is also known that diagnosis of cancer by CT images is difficult to distinguish between peristaltic movement and cancer stenosis in the gastrointestinal tract such as the esophagus. Therefore, in this study, by performing image recognition learning a CT image of a patient diagnosed as esophageal cancer in the past with a convolution neural network (CNN) and a recurrent neural network (LSTM), it is aimed to construct a system for discriminating the presence or absence of cancer from a new CT image. As a result, we succeeded in a classification model of esophageal cancer using CNN and LSTM, and it to classify with more than 80% accuracy.