Inferior alveolar canal segmentation in cone beam computed tomography images using an adaptive diffusion flow active contour model

The success of dental implant surgery is subject to accurate advanced planning. In order to properly plan for suitable implant placement, it is necessary for accurate segmentation of the inferior alveolar canal. This paper presents a new approach of a semi-automatic method based on a new and effective active contour model viz. an adaptive diffusion flow active contour model. Cone-beam computed tomography (CBCT) images is used as a dataset to extract different views of an inferior alveolar canal. The method has been tested in a ground truth set and evaluated using three similar indicators (the Jaccard index, Dice´s coefficient, and Overlap coefficient), achieving promising results in all of them (0.908±0.016, 0.947±0.008, and 0.954±0.008, respectively). Moreover, the presented results show that our method obtains higher accuracy values when compared with GVF snake. The method has proven to be significantly accurate and is possibly integrable in current dental implant surgery planning systems.