Automatic landmark annotation in 3D surface scans of skulls: Methodological proposal and reliability study.

Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.

https://ijai.iaescore.com/index.php/IJAI/article/download/21009/13254

Brain stroke computed tomography images analysis using image processing: A Review

This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.

https://www.beei.org/index.php/EEI/article/download/1827/2053

Proposition of local automatic algorithm for landmark detection in 3D cephalometry

CBCT (Cone Beam Computed Tomography) has fast become a key to produce 3D images in orthodontic and maxilla-facial surgery. Our purpose in this study is to develop a new approach for automatic localization of the region of supraorbital and infraorbital foramina using CBCT images. 19 CBCT images of Moroccan patients were used to validate the proposed algorithm. The automatic algorithm is based on geometric and anatomic information of maxillo-facial structures. The local priori information of supraorbital and infraorbital foramina was used to design the automatic algorithm. The detection rate for supraorbital foramina was 90% and 85% for right and left sides respectively, while the detection rate for infraorbital foramina was 79% and 74% for right and left sides respectively. This study has shown the relevance of local criteria such as geometric and anatomic knowledge in the automation of localization of supraorbital and infraorbital region. This work has gone some way towards the automation of morphometric and anthropometric measurements using CBCT images.

Abdelmajid El moutaouakkil

Papers

Proposition of local automatic algorithm for landmark detection in 3D cephalometry

Automatic Localization of Supraorbital and Infraorbital Foramina Region on CBCT Images