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Accurate automatic quantitative cephalometry are essential for orthodontics. However, manual labeling of cephalometric landmarks is tedious and subjective, which also must be performed by professional doctors. In recent years, deep learning has gained attention for its success in computer vision field. It has achieved large progress in resolving problems like image classification or image segmentation. In this paper, we propose a two-step method which can automatically detect cephalometric landmarks on skeletal X-ray images. First, we roughly extract a region of interest (ROI) patch for each landmark by registering the testing image to training images, which have annotated landmarks. Then, we utilize pre-trained networks with a backbone of ResNet50, which is a state-of-the-art convolutional neural network, to detect each landmark in each ROI patch. The network directly outputs the coordinates of the landmarks. We evaluate our method on two datasets: ISBI 2015 Grand Challenge in Dental X-ray Image Analysis and our own dataset provided by Shandong University. The experiments demonstrate that the proposed method can achieve satisfying results on both SDR (Successful Detection Rate) and SCR (Successful Classification Rate). However, the computational time issue remains to be improved in the future. a global-context shape model. In 2017, Ibragimov et al. added a convolutional neural network for binary classification to their conventional method. They surpass the result of Lindner's a little bit, with around 75.3% prediction accuracy within a 2-mm range [9]. In 2017, Hansang Lee et al. proposed a deep learning based method which achieved not bad results but in a small resized image. He trained two networks to regress the landmark's x and y coordinate directly [10]. In 2019, Jiahong Qian et al. proposed a new architecture called CephaNet which improves the architecture of Faster R-CNN [11,12].Its accuracy is nearly 6% higher than other conventional methods.Despite the variety of techniques available, automatic cephalometric landmark detection remains insufficient due to its limited accuracy. In recent years, deep learning has gained attention for its success in computer vision field. For example, convolutional neural network models are widely used in problems like landmark detection [13,14], image classification [15][16][17] and image segmentation [18,19]. Trained models' performances surpass that of human beings in many applications. Since direct regression of several landmarks is a highly non-linear mapping, which is difficult to learn [20][21][22][23]. In our method, we only try to detect one key point in one patch image. We learn a non-linear mapping function for only one key point. Each key point has its corresponding non-linear mapping function. So we can achieve more accurate detection of key point than other methods.In this paper, we propose a two-step method for the automatic detection of cephalometric landmarks. First, we get the coarse landmark location by registering the test image to a most similar image in...

Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.

Objectives: To provide insight into the biological effects of activated Yes-associated protein (YAP) on the proliferation, apoptosis, and senescence of human periodontal ligament stem cells (h-PDLSCs).Methods: h-PDLSCs were isolated by the limiting dilution method, and their surface markers were quantified by flow cytometry. Enhanced green fluorescence protein (EGFP)-labeled lentiviral vector was used to activate YAP in h-PDLSCs, then qRT-PCR and Western blotting were used to evaluate the expression level of YAP. Immunofluorescence was used to detect the location of YAP in h-PDLSCs. The proliferation activity was detected by cell counting kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU), and the cell cycle was determined by flow cytometry. Apoptosis was analyzed by Annexin V-APC staining. Cell senescence was detected by β-galactosidase staining. Proteins in ERK, Bcl-2, and p53 signaling pathways were detected by Western blotting.Results: h-PDLSCs were isolated successfully and were positive for human mesenchymal stem cell surface markers. After YAP was activated by lentiviral vector, the mRNA and protein of YAP were highly expressed, and more YAP translocated into the nucleus. When YAP was overexpressed in h-PDLSCs, proliferation activity was improved; early and late apoptosis rates decreased (P<0.05); the proportion of cells in G2/M phases increased (P<0.05), while that in G0/G1 phase decreased (P<0.05); cellular senescence was delayed (P<0.01); the expression of P-MEK, P-ERK, P-P90RSK and P-Msk increased, while the expression of Bcl-2 family members (Bak, Bid and Bik) decreased.Conclusions: Activated YAP promotes proliferation, inhibits apoptosis, and delays senescence of h-PDLSCs. The Hippo-YAP signaling pathway can influence ERK and Bcl-2 signaling pathways.