Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN
| Autor: | David M. Alfi, Yankun Lang, Hannah H. Deng, Tianshu Kuang, James J. Xia, Deqiang Xiao, Li Wang, Chunfeng Lian, Peng Yuan, Steve Guofang Shen, Jaime Gateno, Dinggang Shen, Pew Thian Yap, Kim-Han Thung |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science Computed tomography 02 engineering and technology 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Computer vision Artificial intelligence Scale (map) business |
| Zdroj: | Graph Learning in Medical Imaging ISBN: 9783030358167 GLMI@MICCAI |
| DOI: | 10.1007/978-3-030-35817-4_16 |
| Popis: | Craniomaxillofacial (CMF) landmark localization is an important step for characterizing jaw deformities and designing surgical plans. However, due to the complexity of facial structure and the deformities of CMF patients, it is still difficult to accurately localize a large scale of landmarks simultaneously. In this work, we propose a three-stage coarse-to-fine deep learning method for digitizing 105 anatomical craniomaxillofacial landmarks on cone-beam computed tomography (CBCT) images. The first stage outputs a coarse location of each landmark from a low-resolution image, which is gradually refined in the next two stages using the corresponding higher resolution images. Our method is implemented using Mask R-CNN, by also incorporating a new loss function that learns the geometrical relationships between the landmarks in the form of a root/leaf structure. We evaluate our approach on 49 CBCT scans of patients and achieve an average detection error of 1.75 ± 0.91 mm. Experimental results show that our approach overperforms the related methods in the term of accuracy. |
| Databáze: | OpenAIRE |
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