所属 研究施設 研究施設 職種 教授
|表題||Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery|
|掲載誌名||正式名：The International Journal of Medical Robotics and Computer Assisted Surgery|
|出版社||© 2020 John Wiley & Sons, Ltd.|
|著者・共著者||MA Qingchuan†, KOBAYASHI Etsuko, FAN ,Bowen , NAKAGAWA Keiichi , SAKUMA Ichiro, MASAMUNE Ken, SUENAGA Hideyuki|
Manual landmarking is a time consuming and highly professional work. Although some algorithm‐based landmarking methods have been proposed, they lack flexibility and may be susceptible to data diversity.
The CT images from 66 patients who underwent oral and maxillofacial surgery (OMS) were landmarked manually in MIMICS. Then the CT slices were exported as images for recreating the 3D volume. The coordinate data of landmarks were further processed in Matlab using a principal component analysis (PCA) method. A patch‐based deep neural network model with a three‐layer convolutional neural network (CNN) was trained to obtain landmarks from CT images.