V-Net: An Attention-guided Volumetric Regression Network for Tooth Landmark Localization on CT Images with Metal Artifacts.

For virtual surgical planning in orthognathic surgery, marking tooth landmarks on CT images is an important procedure. However, the manual localization procedure of tooth landmarks is time-consuming, labor-intensive, and requires expert knowledge. Also, direct and automatic tooth landmark localization on CT images is difficult because of the lower resolution and metal artifacts of dental images.

Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images.

Background: The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity.

Methods: The 2D, 2.

Ceph-Net: automatic detection of cephalometric landmarks on scanned lateral cephalograms from children and adolescents using an attention-based stacked regression network.

Background: The success of cephalometric analysis depends on the accurate detection of cephalometric landmarks on scanned lateral cephalograms. However, manual cephalometric analysis is time-consuming and can cause inter- and intra-observer variability. The purpose of this study was to automatically detect cephalometric landmarks on scanned lateral cephalograms with low contrast and resolution using an attention-based stacked regression network (Ceph-Net).

Automatic classification of 3D positional relationship between mandibular third molar and inferior alveolar canal using a distance-aware network.

The purpose of this study was to automatically classify the three-dimensional (3D) positional relationship between an impacted mandibular third molar (M3) and the inferior alveolar canal (MC) using a distance-aware network in cone-beam CT (CBCT) images. We developed a network consisting of cascaded stages of segmentation and classification for the buccal-lingual relationship between the M3 and the MC. The M3 and the MC were simultaneously segmented using Dense121 U-Net in the segmentation stage, and their buccal-lingual relationship was automatically classified using a 3D distance-aware network with the multichannel inputs of the original CBCT image and the signed distance map (SDM) generated from the segmentation in the classification stage.

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning.

The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to validate the accuracy and reliability of BMD measurements made using quantitative cone-beam CT (CBCT) image based on deep learning by applying the method to clinical data from actual patients. Datasets containing 7500 pairs of CT and CBCT axial slice images from 30 patients were used to train a previously developed deep-learning model (QCBCT-NET).

SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network.

The objective of this study was to automatically classify surgical plans for maxillary sinus floor augmentation in implant placement at the maxillary posterior edentulous region using a 3D distance-guided network on CBCT images. We applied a modified ABC classification method consisting of five surgical approaches for the deep learning model. The proposed deep learning model (SinusC-Net) consisted of two stages of detection and classification according to the modified classification method.

Structure-preserving quality improvement of cone beam CT images using contrastive learning.

Cone-beam CT (CBCT) is widely used in dental clinics but exhibits limitations in assessing soft tissue pathology because of its lack of contrast resolution and low Hounsfield Units (HU) quantification accuracy. We aimed to increase the image quality and HU accuracy of CBCTs while preserving anatomical structures. We generated CT-like images from CBCT images using a patchwise contrastive learning-based GAN model.

Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network.

Objectives: The purpose of this study was to automatically diagnose odontogenic cysts and tumors of both jaws on panoramic radiographs using deep learning. We proposed a novel framework of deep convolution neural network (CNN) with data augmentation for detection and classification of the multiple diseases.

Methods: We developed a deep CNN modified from YOLOv3 for detecting and classifying odontogenic cysts and tumors of both jaws.

Quantitative measurement of peri-implant bone defects using optical coherence tomography.

Purpose: The purpose of this study was to visualize and identify peri-implant bone defects in optical coherence tomography (OCT) images and to obtain quantitative measurements of the defect depth.

Methods: Dehiscence defects were intentionally formed in porcine mandibles and implants were simultaneously placed without flap elevation. Only the threads of the fixture could be seen at the bone defect site in the OCT images, so the depth of the peri-implant bone defect could be measured through the length of the visible threads.

Tooth cracks detection and gingival sulcus depth measurement using optical coherence tomography.

The aims of this study were to develop an automatic detection technique for tooth cracks and to suggest quantitative methods for measuring gingival sulcus depth using swept-source optical coherence tomography (SS-OCT). We evaluated SS-OCT with wavelength centered at 1310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks and gingival sulcus depth. The reliability of the SS-OCT images was verified by imaging the crack in extracted human teeth and gingival sulcus of porcine sample.

Automatic detection of tooth cracks in optical coherence tomography images.

Purpose: The aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging.

Methods: We evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods.

Improved accuracy in periodontal pocket depth measurement using optical coherence tomography.

Purpose: The purpose of this study was to examine whether periodontal pocket could be satisfactorily visualized by optical coherence tomography (OCT) and to suggest quantitative methods for measuring periodontal pocket depth.

Methods: We acquired OCT images of periodontal pockets in a porcine model and determined the actual axial resolution for measuring the exact periodontal pocket depth using a calibration method. Quantitative measurements of periodontal pockets were performed by real axial resolution and compared with the results from manual periodontal probing.

The relationship between dental implant stability and trabecular bone structure using cone-beam computed tomography.

Purpose: The objective of this study was to investigate the relationships between primary implant stability as measured by impact response frequency and the structural parameters of trabecular bone using cone-beam computed tomography(CBCT), excluding the effect of cortical bone thickness.

Methods: We measured the impact response of a dental implant placed into swine bone specimens composed of only trabecular bone without the cortical bone layer using an inductive sensor. The peak frequency of the impact response spectrum was determined as an implant stability criterion (SPF).

Relationship between physical factors and subjective image quality of cone-beam computed tomography images according to diagnostic task.

Objective: This study was designed to investigate the relationship between physical factors and the subjective quality of cone beam computed tomography (CBCT) images used for different diagnostic tasks.

Study Design: CBCT images of a real skull phantom and a SedentexCT IQ phantom were acquired under different exposure conditions (one Dinnova3 CBCT scanner, 60-110 kV and 4-10 mA). Radiologists evaluated subjective image quality of real skull phantom images for each diagnostic task.

Radiation dose reduction in CBCT imaging using K-edge filtering and energy weighting.

This paper presents K-edge filtering and energy weighting methods which enhance the contrast with less radiation does. Usually, energy weighting methods are used with photon-counting detector based CT for each energy bin data obtained to enhance the quality of image. However, we used these methods combine with K-edge filtering in energy-integrating detector.

Material classification of multi-energy CT images using multiple discriminant analysis.

Energy resolved photon-counting detectors could achieve more than one spectral measurement. The goal of this study is to investigate, with experiment, the ability to decompose five materials using energy discriminating detectors and multiple discriminant analysis (MDA). A small field-of-view multi-energy CT system was built.