Systematic Review of Image Segmentation Programs in Craniomaxillofacial Surgery

Three-dimensional surgical simulation

To What Extent is Each Area of Oral-Maxillofacial Surgery Practiced in the United States Today?

Image segmentation, i.e., identification of homogeneous regions in the image, has been the subject of considerable research activity over the last three decades. Segmentation of images is a major task of image processing. There is no general segmentation procedure that can deal with all sorts of images, and the correct solution will always depend to a certain degree on subjectivity. Many algorithms have been elaborated for gray scale images. Those algorithms are based on different methods including: classification-based methods, edge-based methods, region-based methods, and hybrid methods. Iterative Self-Organizing Data Analysis Technique (ISODATA) is one of the classification-based methods in image segmentation. It is an unsupervised learning Technique. Statistical approach is wieldy used in image processing in order to model the data of image. Gaussian and Gamma distributions have been used in this technique. Gaussian can only approximate a symmetric shape of histogram. Gamma distribution can only approximate a symmetric and a skewed to right shapes of the histogram. However, Beta distribution is more general than Gaussian and Gamma, and it can approximate any shape of histogram as skewed to left, skewed to right, and symmetric. The algorithm developed here is based on the technique of unsupervised learning using a mixture of Beta distributions. Experimental results are presented to show good performance on segmentation of skin images.

David Stanley Precious (1944-2015)

S124: Open and Closed Techniques for Upper Facial Rejuvenation

Infinite cornucopia: The future of education and training in oral and maxillofacial surgery

3D point cloud segmentation is one of the key steps in point cloud processing, which is the technology and process of dividing the point cloud data set into several specific regions with unique properties and proposing interesting targets. It has important applications in medical image processing, industrial inspection, cultural relic’s identification and 3D visualization. Despite widespread use, point cloud segmentation still faces many challenges because of uneven sampling density, high redundancy, and lack explicit structure of point cloud data. The main goal of this paper is to analyse the most popular algorithms and methodologies to segment point clouds. To facilitate analysis and summary, according to the principle of segmentation we divide the 3D point cloud segmentation methods into edge-based methods, region-based methods, graph-based methods, model-based methods, and machine learning-based methods. Then analyze and discuss the advantages, disadvantages and application scenarios of these segmentation methods. For some algorithms the results of the segmentation and classification is shown. Finally, we outline the issues that need to be addressed and important future research directions.

Image segmentation is a fundamental step in many applications of image processing. Many image segmentation techniques exist based on different methods such as classification-based methods, edge-based methods, region-based methods, and hybrid methods. The principal approach of segmentation is based on thresholding (classification) that is related to thresholds estimation problem. The ISODATA (Iterative Self-Organizing Data Analysis Technique) method is one of the classification-based methods in image segmentation. We assumed that the data in images is modeled by Gamma distribution. The objective of this paper is to explain a new method that combines Gamma distribution with the technique of ISODATA. The algorithm has two phases: splitting using Gamma distribution then merging which are done based on some predefined parameters. Experimental results showed good segmentation for artificial and real images.

Background Cleft lip and palate occupy a high proportion in congenital diseases. Pain of cleft lip and palate repair surgery greatly influences feeding and wound healing during perioperation. Content This article present the effect of maxillofacial nerve block in cleft lip and palate repair surgery on perioperative analgesia, postoperative revival, relevant complications and postoperative analgesic. Objective By summarizing the studies of maxillofacial nerve block, discussing the positive effect and side effect of maxillofacial nerve blocking used for cleft lip and palate repair surgery, this review presents the effective proof of analgesia for cleft lip and palate repair surgery. Trend Maxillofacial nerve block is a relatively ideal way for cleft lip and palate repair surgery, especially after ultrasonic introduced, that the related complications was falling. At present, it is still lack of an absolutely satisfying and safe way for maxillofacial nerve block. So the related contents still need further clinical study. Key words: Cleft lip and palate repair surgery; Infraorbital never; Maxillary never; Analgesia

Image recognition and image processing usually contain the technique of image segmentation. Excellent segmentation results can directly affect the accuracy of image recognition and processing. The essence of image segmentation is to segment each frame of a certain image or a video into multiple specific objects or regions and represent them with different labels. This paper focuses on the segmentation results obtained in image segmentation of images used for intelligent monitoring of Mandarin exams are usually visualized for image analysis. In this paper, we first investigate the performance improvement techniques for semantic segmentation in the image segmentation task for intelligent monitoring of Mandarin exams, improve the pixel classification capability by performing semantic migration, and, for the first time, extend the dataset substantially by style transformation to improve the model’s recognition of advanced features. In addition, to further address the shortcomings of the dataset, this paper improves the performance of image segmentation using synthetic datasets by investigating synthetic dataset image segmentation improvement techniques that reduce the reliance on manually annotated datasets. Image segmentation techniques continue to advance, and there are even thousands of commonly used segmentation methods for image segmentation development to date. Among them, they can be broadly classified as region-based segmentation methods, threshold-based segmentation methods, edge-based segmentation methods, specific theory-based segmentation methods, and deep learning-based segmentation methods. However, the methods used in this paper have all been experimentally demonstrated to improve the effectiveness of the techniques and proved to outperform other existing methods in the same field in the publicly available datasets LSUN, Cityscapes, and GTA5 datasets, respectively.

To evaluate effects of preoperative virtual planning and jaw reconstruction guided by dental implant rehabilitation on dental prosthesis rehabilitation after jaw reconstruction. Patients indicated for segmental jaw resection and who agreed to receive jaw reconstruction procedures were enrolled in the study. Appropriate surgical procedures were determined by a maxillofacial surgeon and a prosthodontist before surgery. The virtual design was created according to preoperative computed tomography. Patients were divided into navigation and non-navigation groups. Implant surgery was performed 6 months after reconstruction surgery. After treatment completion, factors such as survival rate of implants, site of reconstruction, type of graft, and type of prosthesis were compared. In total, 29 patients were included in the study, with 16 patients in the non-navigation group and 13 in the navigation group. A total of 101 implants were inserted, and the implant success rate was 98.02% (2 implants extracted due to peri-implantitis). All patients received prosthetic treatment. Of the 13 navigation group patients, 9 received fixed implant-supported prostheses, whereas the other 4 received removable dentures. Of the 16 non-navigation group patients, 9 eventually received fixed implant-supported prostheses and 7 received removable dentures. There were no significant intergroup differences in terms of prosthesis type (P = .702). However, the proportion of fixed implant-supported prostheses in the navigation group was higher compared with the non-navigation group. Preoperative virtual planning and dental implant rehabilitation-guided jaw reconstruction through preoperative designing can provide a good opportunity to achieve high rates of implant success and dental rehabilitation. This method can also benefit fixed implant-supported prosthetic restorations. Moreover, the use of navigation after virtual planning has no effect on the type of prosthetic reconstruction.

Velopharyngeal Insufficiency (VPI) is often occur in cleft palate patient. Furlow double opposing Z plasty technique, based on several studies, have shown better speech and velopharyngeal competence outcome compare to other techniques. The objective of this case report is to describe Furlow double opposing Z plasty technique procedure with buccal fat pad in wide cleft adult patient and management of intraoperative bleeding complications that occur. A17 years old male patient diagnosed with unilateral palatoschisis complete post labioplasty and Furlow palatoplasty technique was performed with modifications using pedicle buccal fat pad. While the procedure ongoing, bleeding occurs which suspected to originate from the ascending palatine artery and the bleeding management was performed. Proper management from the oral surgeon and the anesthesia team has been successfully manage the bleeding. There were no complications after a few days of observation. Furlow double opposing Z plasty with buccal fat pad can be used as an alternative technique for cleft palate closure in adult patients.

Craniomaxillofacial osteosarcoma — The role of surgical margins

All of Us

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.