Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation. Methods: To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling. Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation

Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection images

This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. It can be used for a digital platform of 3D jaw-teeth-face models in a variety of applications, including 3D digital treatment planning and orthognathic surgery. Difficulties in accurately merging facial scans and CBCT images are due to the different image acquisition methods and limited area of correspondence between the two facial surfaces. In addition, it is difficult to use machine learning techniques because they use face-related 3D medical data with radiation exposure, which are difficult to obtain for training. The proposed method addresses these problems by reusing an existing machine-learning-based 2D landmark detection algorithm in an open-source library and developing a novel mathematical algorithm that identifies paired 3D landmarks from knowledge of the corresponding 2D landmarks. A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models. Note that this reduces a 3D landmark detection problem to a 2D problem of identifying the corresponding landmarks on two 2D projection images generated from two different projection angles. Here, the 3D landmarks for registration were selected from the sub-surfaces with the least geometric change under the CBCT and face scan environments. For the final fine-tuning of the registration, the Iterative Closest Point method was applied, which utilizes geometrical information around the 3D landmarks. The experimental results show that the proposed method achieved an averaged surface distance error of 0.74 mm for three pairs of CBCT and face scan datasets.Comment: 8 pages, 6 figures, 2 table

Melittin restores proteasome function in an animal model of ALS

Amyotrophic lateral sclerosis (ALS) is a paralyzing disorder characterized by the progressive degeneration and death of motor neurons and occurs both as a sporadic and familial disease. Mutant SOD1 (mtSOD1) in motor neurons induces vulnerability to the disease through protein misfolding, mitochondrial dysfunction, oxidative damage, cytoskeletal abnormalities, defective axonal transport- and growth factor signaling, excitotoxicity, and neuro-inflammation

Effects of Bee Venom on Glutamate-Induced Toxicity in Neuronal and Glial Cells

Bee venom (BV), which is extracted from honeybees, is used in traditional Korean medical therapy. Several groups have demonstrated the anti-inflammatory effects of BV in osteoarthritis both in vivo and in vitro. Glutamate is the predominant excitatory neurotransmitter in the central nervous system (CNS). Changes in glutamate release and uptake due to alterations in the activity of glutamate transporters have been reported in many neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis. To assess if BV can prevent glutamate-mediated neurotoxicity, we examined cell viability and signal transduction in glutamate-treated neuronal and microglial cells in the presence and absence of BV. We induced glutamatergic toxicity in neuronal cells and microglial cells and found that BV protected against cell death. Furthermore, BV significantly inhibited the cellular toxicity of glutamate, and pretreatment with BV altered MAP kinase activation (e.g., JNK, ERK, and p38) following exposure to glutamate. These findings suggest that treatment with BV may be helpful in reducing glutamatergic cell toxicity in neurodegenerative diseases

Mandibular Vertical Growth Deficiency After Botulinum-Induced Hypotrophy of Masticatory Closing Muscles in Juvenile Nonhuman Primates

The purpose of this study was to investigate the relationship between masticatory muscular hypotrophy and mandibular growth in juvenile nonhuman primates (cynolmolgus monkeys, Macaca fasicularis). We hypothesized that botulinum toxin (BTX)-induced neuro-muscular junctional block and its resultant hypotrophy of masticatory muscles would produce mandibular growth disturbances in size and shape. Ten male cynomolgus monkeys were divided into three groups: group I (control; n = 3), group II (unilateral BTX; n = 4), and group III (bilateral BTX; n = 3). The unilateral or bilateral muscular hypotrophy of major masticatory closing muscles was induced by synchronous BTX application to masseter, medial pterygoid, and temporal muscle. Mandibular growth was tracked by linear, angular, area and volume measurements using three-dimensional (3D) computed tomography imaging before BTX treatment and after 3 and 6 months. After unilateral hypotrophy of masticatory muscles in group II, vertical growth deficiency was prominent on the BTX side, with compensatory overgrowth on the control side. The bilateral muscular hypotrophy in group III also showed smaller ramal height and width than that of control (group I) and control side (group II). Moreover, ramal sagittal angles (posterior tilt) increased on the BTX side of both groups II and III, but coronal angles (lateral tilt) did so on the BTX side of group II, resulting in asymmetry. The results confirmed our hypothesis that functional activity of masticatory closing muscles is closely related to mandibular growth in size and shape of juvenile nonhuman primates. In addition, the focused growth disturbances on the ramal height and posterior-lateral tilt suggested the possible role of masticatory closing muscles for ramal vertical and angular growth vector of the mandible

Intraosseous Nerve Sheath Tumors in the Jaws

Although the head and neck region is recognized as the most common location for peripheral nerve sheath tumors, central involvement, particularly in the jaw bones, is quite unusual. Neurofibroma is one of the most common nerve sheath tumors occurring in the soft tissue and generally appears in neurofibromatosis 1 (NF1 or von Recklinghausen's disease). Malignant peripheral nerve sheath tumors (MPNSTs) are uncommon sarcomas that almost always arise in the soft tissue. Here, we report four cases of intraosseous peripheral nerve sheath tumors occurring in the jaw bones and compare the clinical, radiologic, and pathologic findings in order to make a differential diagnosis

Metal Artifact Reduction in CT by identifying missing data hidden in metals

There is increasing demand in the field of dental and medical radiography for effective metal artifact reduction (MAR) in computed tomography (CT) because artifact caused by metallic objects causes serious image degradation that obscures information regarding the teeth and/or other biological structures. This paper presents a new MAR method that uses the Laplacian operator to reveal background projection data hidden in regions containing data from metal. In the proposed method, we attempted to decompose the projection data into two parts: data from metal only (metal data), and background data in the absence of metal. Removing metal data from the projections enables us to perform sparsity-driven reconstruction of the metal component and subsequent removal of the metal artifact. The results of clinical experiments demonstrated that the proposed MAR algorithm improves image quality and increases the standard of 3D reconstruction images of the teeth and mandible

Reference landmarks.

These are easy-to-find through CNN with input of the illuminated images because they have strong geometric cues that can be revealed in illuminated 2D images.</p