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MASAMUNE Ken
Department Research Institutes and Facilities, Research Institutes and Facilities Position Professor |
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| Article types | Original article |
| Language | English |
| Peer review | Peer reviewed |
| Title | Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery |
| Journal | Formal name：The International Journal of Medical Robotics and Computer Assisted Surgery ISSN code：1478-596X |
| Domestic / Foregin | Foregin |
| Publisher | © 2020 John Wiley & Sons, Ltd. |
| Volume, Issue, Page | 16(3),pp.e2093 |
| Author and coauthor | MA Qingchuan†, KOBAYASHI Etsuko, FAN ,Bowen , NAKAGAWA Keiichi , SAKUMA Ichiro, MASAMUNE Ken, SUENAGA Hideyuki* |
| Publication date | 2020/06 |
| Summary | Background
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. Methods 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. |
| DOI | 10.1002/rcs.2093 |
| PMID | 32065718 |