Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement

Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance segmentation methods using convolutional neural networks have demonstrated ground-breaking results on individual tooth segmentation tasks, and are used in various medical imaging applications. While point-based detection networks achieve superior results on dental images, it is still a challenging task to distinguish adjacent teeth because of their similar topologies and proximate nature. In this study, we propose a point-based tooth localization network that effectively disentangles each individual tooth based on a Gaussian disentanglement objective function. The proposed network first performs heatmap regression accompanied by box regression for all the anatomical teeth. A novel Gaussian disentanglement penalty is employed by minimizing the sum of the pixel-wise multiplication of the heatmaps for all adjacent teeth pairs. Subsequently, individual tooth segmentation is performed by converting a pixel-wise labeling task to a distance map regression task to minimize false positives in adjacent regions of the teeth. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of detection by 9.1%, which results in a high performance in terms of individual tooth segmentation. The primary significance of the proposed method is two-fold: 1) the introduction of a point-based tooth detection framework that does not require additional classification and 2) the design of a novel loss function that effectively separates Gaussian distributions based on heatmap responses in the point-based detection framework.Comment: 11 pages, 7 figure

Cylindrical Tightly Coupled Dipole Array Antenna

A cylindrical tightly coupled dipole array antenna (C-TCDA) based on Munk’s planar TCDA theory, the elements of which are placed on the surface of a cylinder, is introduced and analyzed in this study. The explicit field components of the transverse electromagnetic (TEM) waves existing in the C-TCDA for both polarizations are given. Moreover, the embedding impedance characteristics of the C-TCDA are presented and compared with those of planar TCDA. Analysis results show that the C-TCDA has similar impedance characteristics to the planar TCDA and thus can be implemented in a similar manner. The design of a dual-polarized omnidirectional octagonal TCDA, which has a 3.08:1 bandwidth (from 0.73 GHz to 2.25 GHz) with a voltage standing wave radiation (VSWR) of less than 2 and a low gain variation of less than 1.8 dB at broadside for both polarizations, is also proposed. When scanning up to ±30° in the θ-direction, the array operates in the same frequency band with a VSWR of less than 2.05 and a low gain variation of less than 2 dB for both polarizations

Heterogeneity of Skin Surface Oxygen Level of Wrist in Relation to Acupuncture Point

The distribution of partial oxygen pressure (pO2) is analyzed for the anterior aspect of the left wrist with an amperometric oxygen microsensor composed of a small planar Pt disk-sensing area (diameter = 25 μm). The pO2 levels vary depending on the measurement location over the wrist skin, and they are systematically monitored in the analysis for both one-dimensional single line (along the wrist transverse crease) and two-dimensional square area of the wrist region. Relatively higher pO2 values are observed at certain area in close proximity to the position of acupuncture points with statistical significance, indicating strong relationship between oxygen and acupuncture point. The used oxygen microsensor is sensitive enough to detect the pO2 variation depending on the location. This study may provide information helpful to understand possible physiological roles of the acupuncture points