Automatic Registration Between Dental Cone-Beam CT and Scanned Surface via Deep Pose Regression Neural Networks and Clustered Similarities.

Computerized registration between maxillofacial cone-beam computed tomography (CT) images and a scanned dental model is an essential prerequisite for surgical planning for dental implants or orthognathic surgery. We propose a novel method that performs fully automatic registration between a cone-beam CT image and an optically scanned model. To build a robust and automatic initial registration method, deep pose regression neural networks are applied in a reduced domain (i.e., two-dimensional image). Subsequently, fine registration is performed using optimal clusters. A majority voting system achieves globally optimal transformations while each cluster attempts to optimize local transformation parameters. The coherency of clusters determines their candidacy for the optimal cluster set. The outlying regions in the iso-surface are effectively removed based on the consensus among the optimal clusters. The accuracy of registration is evaluated based on the Euclidean distance of 10 landmarks on a scanned model, which have been annotated by experts in the field. The experiments show that the registration accuracy of the proposed method, measured based on the landmark distance, outperforms the best performing existing method by 33.09%. In addition to achieving high accuracy, our proposed method neither requires human interactions nor priors (e.g., iso-surface extraction). The primary significance of our study is twofold: 1) the employment of lightweight neural networks, which indicates the applicability of neural networks in extracting pose cues that can be easily obtained and 2) the introduction of an optimal cluster-based registration method that can avoid metal artifacts during the matching procedures.

Interactive registration between supine and prone scans in computed tomography colonography using band-height images.

In computed tomographic colonography (CTC), a patient is commonly scanned twice including supine and prone scans to improve the sensitivity of polyp detection. Typically, a radiologist must manually match the corresponding areas in the supine and prone CT scans, which is a difficult and time-consuming task, even for experienced scan readers. In this paper, we propose a method of supine-prone registration utilizing band-height images, which are directly constructed from the CT scans using a ray-casting algorithm containing neighboring shape information. In our method, we first identify anatomical feature points and establish initial correspondences using local extreme points on centerlines. We then correct correspondences using band-height images that contain neighboring shape information. We use geometrical and image-based information to match positions between the supine and prone centerlines. Finally, our algorithm searches the correspondence of user input points using the matched anatomical feature point pairs as key points and band-height images. The proposed method achieved accurate matching and relatively faster processing time than other previously proposed methods. The mean error of the matching between the supine and prone points for uniformly sampled positions was 18.41±22.07mm in 20 CTC datasets. The average pre-processing time was 62.9±8.6s, and the interactive matching was performed in nearly real-time. Our supine-prone registration method is expected to be helpful for the accurate and fast diagnosis of polyps.

The Investigation on the Distribution of Malassezia Yeasts on the Normal Korean Skin by 26S rDNA PCR-RFLP.

BACKGROUND: Malassezia yeasts are normal flora of the skin that are discovered in 75~98% of health subjects, but since its association with various skin disorders have been known, many studies have been conducted in the distribution of the yeasts. OBJECTIVE: To isolate, identify, and classify Malassezia yeasts from the normal human skin of Koreans by using the rapid and accurate molecular biology method (26S rDNA PCR-RFLP) which overcome the limits of morphological and biochemical methods, and to gather a basic database that will show its relation to various skin diseases. METHODS: Malassezia yeasts were cultured from clinically healthy human skin using scrub-wash technique at five sites (forehead, cheek, chest, upper arm, and thigh) and swabbing technique at scalp in 160 participants comprised of 80 males and 80 females aged from 0 to 80. Identification of obtained strains were placed into the one of the 11 species by 26S rDNA PCR-RFLP. RESULTS: An overall positive culture rate was 62.4% (599/960). As shown in the experiment groups by their age, the positive culture rate was the highest (74.2%) in the age 21~30 and 31~40 (89/120). In the experiment groups by different body areas, the scalp showed the highest positive culture rate of 90% (144/160). On analysis of 26S rDNA PCR-RFLP, M. globosa was the most predominant species in the age 0~10 (32.8%), 11~20 (28.9%), 21~30 (32.3%). M. restricta was identified as predominant species in the age 41~50 (27.9%), 61~70 (31.5%) and 71~80 (24.0%). In the age 31~40 years, M. sympodialis was found to be the most common species (24.6%). According to body site, M. restricta was more frequently recovered in the scalp (56.8%), forehead (39.8%) and cheek (24.0%) and while M. globosa was more frequently recovered in the chest (36.8%). Higher positive culture rates of Malassezia yeasts were shown in male subjects than female counterparts in all body areas except scalp (p<0.05). Especially in this study, M. dermatis, newly isolated Malassezia species from atopic dermatitis patient in Japan, was isolated and identified in 19 cases (1.9%) in healthy subjects. CONCLUSION: The key is to recognize the existence of a difference in the type of Malassezia species in different ages as well as body areas, which reflects differing skin lipid levels in various ages and different body areas. Moreover, 26S rDNA PCR-RFLP analysis which was opted in this study could provide a sensitive and rapid identification system for Malassezia species, which may be applied to epidemiological surveys and clinical practice.