Automatic segmentation of mandibular canal in cone beam CT images using conditional statistical shape model and fast marching
| Autor: | Yoshito Otake, Fatemeh Abdolali, Maryam Abdolali, Yoshinobu Sato, Futoshi Yokota, Reza Aghaeizadeh Zoroofi |
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| Rok vydání: | 2016 |
| Předmět: |
medicine.medical_specialty
Cone beam computed tomography Computer science Biomedical Engineering Mandibular canal Health Informatics Mandible Surgical planning Sensitivity and Specificity 030218 nuclear medicine & medical imaging Mental foramen 03 medical and health sciences 0302 clinical medicine stomatognathic system medicine Preprocessor Humans Radiology Nuclear Medicine and imaging Segmentation Fast marching method business.industry Statistical model Pattern recognition 030206 dentistry General Medicine Cone-Beam Computed Tomography Models Theoretical Computer Graphics and Computer-Aided Design Computer Science Applications medicine.anatomical_structure Surgery Computer Vision and Pattern Recognition Radiology Artificial intelligence business |
| Zdroj: | International journal of computer assisted radiology and surgery. 12(4) |
| ISSN: | 1861-6429 |
| Popis: | Accurate segmentation of the mandibular canal in cone beam CT data is a prerequisite for implant surgical planning. In this article, a new segmentation method based on the combination of anatomical and statistical information is presented to segment mandibular canal in CBCT scans. Generally, embedding shape information in segmentation models is challenging. The proposed approach consists of three main steps as follows: At first, a method based on low-rank decomposition is proposed for preprocessing. Then, a conditional statistical shape model is trained, and mandibular bone is segmented with high accuracy. In the final stage, fast marching with a new speed function is utilized to find the optimal path between mandibular and mental foramen. Fast marching tries to find the darkest tunnel close to the initial segmentation of the canal, which was obtained with conditional SSM model. In this regard, localization of mandibular canal is performed more accurately. The method is applied to the identification of mandibular canal in 120 sets of CBCT images. Conditional statistical model is evaluated by calculating the compactness capacity, specificity and generalization ability measures. The capability of the proposed model is evaluated in the segmentation of mandibular bone and canal. The framework is effective in noisy scans and is able to detect canal in cases with mild bone resorption. Quantitative analysis of the results shows that the method performed better than two other recent methods in the literature. Experimental results demonstrate that the proposed framework is effective and can be used in computer-guided dental implant surgery. |
| Databáze: | OpenAIRE |
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